Podsumowanie analizy

Analizowany zbiór danych posiada bardzo dużą liczbę klas (2696) oraz zmiennych (796). Analizę utrudnia również duża liczba wartości NA. Zbadanie korelacji występujących w zbiorze pokazuje że wiele kolumn niesie podobne informacje, co można wykorzystać w ograniczeniu kolumn użytych w regresji i klasyfikacji. W szczególności podobne są korelacje między kolumnami w ramach zbiorów part_XX zawierających wartości dla różnych progów odcięcia. Wyznaczenie najpopularniejszych klas w zbiorze pokazuje bardzo nierównomierny rozkład danych - po przefiltrowaniu danych tylko 18 klas posiada więcej niż 100 obserwacji. Z tego powodu stworzenie klasyfikatora uwzględniającego wszyskie klasy w praktyce nie ma sensu. Dosyć dokładnie możemy przewidywać tylko kilka najbardziej licznych klas. Dla najpopularniejszych 5 klas można uzyskać dokładnośc (accuracy) na poziomie 0.7. Zwiększając liczbę klas do 20 dokładność ulega istotnemu pogorszeniu i wynosi już tylko nieco ponad 0.55. Z przeprowadzonych eksperymetnów dotyczących predykcji liczby atomów i elektronów wynika, że predykcja taka jest w pewnym stopniu możliwa. W obu przypadkach uzyskano wartości miary R^2 na poziomie ok. 0.56

Przed analizą

Lista wykorzystanych bibliotek

library(plyr)
library(knitr)
library(dplyr)
library(corrplot)
library(grid)
library(reshape)
library(ggplot2)
library(gridExtra)
library(Rmisc)
library(caret)
library(hydroGOF)
set.seed(23)

Powtarzalność wyników

set.seed(23)

Wczytanie danych

naString <- c("nan", "NA","NaN", " ")
data <- read.csv2('./all_summary.txt', na.strings = naString, dec='.')
allClasses <- sapply(data, class)

Usunięcie wierszy

Wstępne przetworzenie danych rozpoczyna się od usunięcia wierszy posiadających określoną wartość zmiennej res_name. Następnie usuwane są wiersze tak aby pary (pdb_code, res_name) były unikalne.

removed_names <- c('DA','DC','DT', 'DU', 'DG', 'DI','UNK', 'UNX', 'UNL', 'PR', 'PD', 'Y1', 'EU', 'N', '15P', 'UQ', 'PX4', 'NAN')
data2 <- filter(data, !(res_name %in% removed_names))

dp <- duplicated(select(data2, pdb_code, res_name))
data3 <- data2[!dp, ]

Analiza danych

Podsumowanie wartości w każdej kolumnie

kable(summary(data3))
title pdb_code res_name res_id chain_id local_BAa local_NPa local_Ra local_RGa local_SRGa local_CCSa local_CCPa local_ZOa local_ZDa local_ZD_minus_a local_ZD_plus_a local_res_atom_count local_res_atom_non_h_count local_res_atom_non_h_occupancy_sum local_res_atom_non_h_electron_sum local_res_atom_non_h_electron_occupancy_sum local_res_atom_C_count local_res_atom_N_count local_res_atom_O_count local_res_atom_S_count dict_atom_non_h_count dict_atom_non_h_electron_sum dict_atom_C_count dict_atom_N_count dict_atom_O_count dict_atom_S_count part_00_blob_electron_sum part_00_blob_volume_sum part_00_blob_parts part_00_shape_O3 part_00_shape_O4 part_00_shape_O5 part_00_shape_FL part_00_shape_O3_norm part_00_shape_O4_norm part_00_shape_O5_norm part_00_shape_FL_norm part_00_shape_I1 part_00_shape_I2 part_00_shape_I3 part_00_shape_I4 part_00_shape_I5 part_00_shape_I6 part_00_shape_I1_norm part_00_shape_I2_norm part_00_shape_I3_norm part_00_shape_I4_norm part_00_shape_I5_norm part_00_shape_I6_norm part_00_shape_I1_scaled part_00_shape_I2_scaled part_00_shape_I3_scaled part_00_shape_I4_scaled part_00_shape_I5_scaled part_00_shape_I6_scaled part_00_shape_M000 part_00_shape_E3_E1 part_00_shape_E2_E1 part_00_shape_E3_E2 part_00_shape_sqrt_E1 part_00_shape_sqrt_E2 part_00_shape_sqrt_E3 part_00_density_O3 part_00_density_O4 part_00_density_O5 part_00_density_FL part_00_density_O3_norm part_00_density_O4_norm part_00_density_O5_norm part_00_density_FL_norm part_00_density_I1 part_00_density_I2 part_00_density_I3 part_00_density_I4 part_00_density_I5 part_00_density_I6 part_00_density_I1_norm part_00_density_I2_norm part_00_density_I3_norm part_00_density_I4_norm part_00_density_I5_norm part_00_density_I6_norm part_00_density_I1_scaled part_00_density_I2_scaled part_00_density_I3_scaled part_00_density_I4_scaled part_00_density_I5_scaled part_00_density_I6_scaled part_00_density_M000 part_00_density_E3_E1 part_00_density_E2_E1 part_00_density_E3_E2 part_00_density_sqrt_E1 part_00_density_sqrt_E2 part_00_density_sqrt_E3 part_01_blob_electron_sum part_01_blob_volume_sum part_01_blob_parts part_01_shape_O3 part_01_shape_O4 part_01_shape_O5 part_01_shape_FL part_01_shape_O3_norm part_01_shape_O4_norm part_01_shape_O5_norm part_01_shape_FL_norm part_01_shape_I1 part_01_shape_I2 part_01_shape_I3 part_01_shape_I4 part_01_shape_I5 part_01_shape_I6 part_01_shape_I1_norm part_01_shape_I2_norm part_01_shape_I3_norm part_01_shape_I4_norm part_01_shape_I5_norm part_01_shape_I6_norm part_01_shape_I1_scaled part_01_shape_I2_scaled part_01_shape_I3_scaled part_01_shape_I4_scaled part_01_shape_I5_scaled part_01_shape_I6_scaled part_01_shape_M000 part_01_shape_E3_E1 part_01_shape_E2_E1 part_01_shape_E3_E2 part_01_shape_sqrt_E1 part_01_shape_sqrt_E2 part_01_shape_sqrt_E3 part_01_density_O3 part_01_density_O4 part_01_density_O5 part_01_density_FL part_01_density_O3_norm part_01_density_O4_norm part_01_density_O5_norm part_01_density_FL_norm part_01_density_I1 part_01_density_I2 part_01_density_I3 part_01_density_I4 part_01_density_I5 part_01_density_I6 part_01_density_I1_norm part_01_density_I2_norm part_01_density_I3_norm part_01_density_I4_norm part_01_density_I5_norm part_01_density_I6_norm part_01_density_I1_scaled part_01_density_I2_scaled part_01_density_I3_scaled part_01_density_I4_scaled part_01_density_I5_scaled part_01_density_I6_scaled part_01_density_M000 part_01_density_E3_E1 part_01_density_E2_E1 part_01_density_E3_E2 part_01_density_sqrt_E1 part_01_density_sqrt_E2 part_01_density_sqrt_E3 part_02_blob_electron_sum part_02_blob_volume_sum part_02_blob_parts part_02_shape_O3 part_02_shape_O4 part_02_shape_O5 part_02_shape_FL part_02_shape_O3_norm part_02_shape_O4_norm part_02_shape_O5_norm part_02_shape_FL_norm part_02_shape_I1 part_02_shape_I2 part_02_shape_I3 part_02_shape_I4 part_02_shape_I5 part_02_shape_I6 part_02_shape_I1_norm part_02_shape_I2_norm part_02_shape_I3_norm part_02_shape_I4_norm part_02_shape_I5_norm part_02_shape_I6_norm part_02_shape_I1_scaled part_02_shape_I2_scaled part_02_shape_I3_scaled part_02_shape_I4_scaled part_02_shape_I5_scaled part_02_shape_I6_scaled part_02_shape_M000 part_02_shape_E3_E1 part_02_shape_E2_E1 part_02_shape_E3_E2 part_02_shape_sqrt_E1 part_02_shape_sqrt_E2 part_02_shape_sqrt_E3 part_02_density_O3 part_02_density_O4 part_02_density_O5 part_02_density_FL part_02_density_O3_norm part_02_density_O4_norm part_02_density_O5_norm part_02_density_FL_norm part_02_density_I1 part_02_density_I2 part_02_density_I3 part_02_density_I4 part_02_density_I5 part_02_density_I6 part_02_density_I1_norm part_02_density_I2_norm part_02_density_I3_norm part_02_density_I4_norm part_02_density_I5_norm part_02_density_I6_norm part_02_density_I1_scaled part_02_density_I2_scaled part_02_density_I3_scaled part_02_density_I4_scaled part_02_density_I5_scaled part_02_density_I6_scaled part_02_density_M000 part_02_density_E3_E1 part_02_density_E2_E1 part_02_density_E3_E2 part_02_density_sqrt_E1 part_02_density_sqrt_E2 part_02_density_sqrt_E3 part_03_blob_electron_sum part_03_blob_volume_sum part_03_blob_parts part_03_shape_O3 part_03_shape_O4 part_03_shape_O5 part_03_shape_FL part_03_shape_O3_norm part_03_shape_O4_norm part_03_shape_O5_norm part_03_shape_FL_norm part_03_shape_I1 part_03_shape_I2 part_03_shape_I3 part_03_shape_I4 part_03_shape_I5 part_03_shape_I6 part_03_shape_I1_norm part_03_shape_I2_norm part_03_shape_I3_norm part_03_shape_I4_norm part_03_shape_I5_norm part_03_shape_I6_norm part_03_shape_I1_scaled part_03_shape_I2_scaled part_03_shape_I3_scaled part_03_shape_I4_scaled part_03_shape_I5_scaled part_03_shape_I6_scaled part_03_shape_M000 part_03_shape_E3_E1 part_03_shape_E2_E1 part_03_shape_E3_E2 part_03_shape_sqrt_E1 part_03_shape_sqrt_E2 part_03_shape_sqrt_E3 part_03_density_O3 part_03_density_O4 part_03_density_O5 part_03_density_FL part_03_density_O3_norm part_03_density_O4_norm part_03_density_O5_norm part_03_density_FL_norm part_03_density_I1 part_03_density_I2 part_03_density_I3 part_03_density_I4 part_03_density_I5 part_03_density_I6 part_03_density_I1_norm part_03_density_I2_norm part_03_density_I3_norm part_03_density_I4_norm part_03_density_I5_norm part_03_density_I6_norm part_03_density_I1_scaled part_03_density_I2_scaled part_03_density_I3_scaled part_03_density_I4_scaled part_03_density_I5_scaled part_03_density_I6_scaled part_03_density_M000 part_03_density_E3_E1 part_03_density_E2_E1 part_03_density_E3_E2 part_03_density_sqrt_E1 part_03_density_sqrt_E2 part_03_density_sqrt_E3 part_04_blob_electron_sum part_04_blob_volume_sum part_04_blob_parts part_04_shape_O3 part_04_shape_O4 part_04_shape_O5 part_04_shape_FL part_04_shape_O3_norm part_04_shape_O4_norm part_04_shape_O5_norm part_04_shape_FL_norm part_04_shape_I1 part_04_shape_I2 part_04_shape_I3 part_04_shape_I4 part_04_shape_I5 part_04_shape_I6 part_04_shape_I1_norm part_04_shape_I2_norm part_04_shape_I3_norm part_04_shape_I4_norm part_04_shape_I5_norm part_04_shape_I6_norm part_04_shape_I1_scaled part_04_shape_I2_scaled part_04_shape_I3_scaled part_04_shape_I4_scaled part_04_shape_I5_scaled part_04_shape_I6_scaled part_04_shape_M000 part_04_shape_E3_E1 part_04_shape_E2_E1 part_04_shape_E3_E2 part_04_shape_sqrt_E1 part_04_shape_sqrt_E2 part_04_shape_sqrt_E3 part_04_density_O3 part_04_density_O4 part_04_density_O5 part_04_density_FL part_04_density_O3_norm part_04_density_O4_norm part_04_density_O5_norm part_04_density_FL_norm part_04_density_I1 part_04_density_I2 part_04_density_I3 part_04_density_I4 part_04_density_I5 part_04_density_I6 part_04_density_I1_norm part_04_density_I2_norm part_04_density_I3_norm part_04_density_I4_norm part_04_density_I5_norm part_04_density_I6_norm part_04_density_I1_scaled part_04_density_I2_scaled part_04_density_I3_scaled part_04_density_I4_scaled part_04_density_I5_scaled part_04_density_I6_scaled part_04_density_M000 part_04_density_E3_E1 part_04_density_E2_E1 part_04_density_E3_E2 part_04_density_sqrt_E1 part_04_density_sqrt_E2 part_04_density_sqrt_E3 part_05_blob_electron_sum part_05_blob_volume_sum part_05_blob_parts part_05_shape_O3 part_05_shape_O4 part_05_shape_O5 part_05_shape_FL part_05_shape_O3_norm part_05_shape_O4_norm part_05_shape_O5_norm part_05_shape_FL_norm part_05_shape_I1 part_05_shape_I2 part_05_shape_I3 part_05_shape_I4 part_05_shape_I5 part_05_shape_I6 part_05_shape_I1_norm part_05_shape_I2_norm part_05_shape_I3_norm part_05_shape_I4_norm part_05_shape_I5_norm part_05_shape_I6_norm part_05_shape_I1_scaled part_05_shape_I2_scaled part_05_shape_I3_scaled part_05_shape_I4_scaled part_05_shape_I5_scaled part_05_shape_I6_scaled part_05_shape_M000 part_05_shape_E3_E1 part_05_shape_E2_E1 part_05_shape_E3_E2 part_05_shape_sqrt_E1 part_05_shape_sqrt_E2 part_05_shape_sqrt_E3 part_05_density_O3 part_05_density_O4 part_05_density_O5 part_05_density_FL part_05_density_O3_norm part_05_density_O4_norm part_05_density_O5_norm part_05_density_FL_norm part_05_density_I1 part_05_density_I2 part_05_density_I3 part_05_density_I4 part_05_density_I5 part_05_density_I6 part_05_density_I1_norm part_05_density_I2_norm part_05_density_I3_norm part_05_density_I4_norm part_05_density_I5_norm part_05_density_I6_norm part_05_density_I1_scaled part_05_density_I2_scaled part_05_density_I3_scaled part_05_density_I4_scaled part_05_density_I5_scaled part_05_density_I6_scaled part_05_density_M000 part_05_density_E3_E1 part_05_density_E2_E1 part_05_density_E3_E2 part_05_density_sqrt_E1 part_05_density_sqrt_E2 part_05_density_sqrt_E3 part_06_blob_electron_sum part_06_blob_volume_sum part_06_blob_parts part_06_shape_O3 part_06_shape_O4 part_06_shape_O5 part_06_shape_FL part_06_shape_O3_norm part_06_shape_O4_norm part_06_shape_O5_norm part_06_shape_FL_norm part_06_shape_I1 part_06_shape_I2 part_06_shape_I3 part_06_shape_I4 part_06_shape_I5 part_06_shape_I6 part_06_shape_I1_norm part_06_shape_I2_norm part_06_shape_I3_norm part_06_shape_I4_norm part_06_shape_I5_norm part_06_shape_I6_norm part_06_shape_I1_scaled part_06_shape_I2_scaled part_06_shape_I3_scaled part_06_shape_I4_scaled part_06_shape_I5_scaled part_06_shape_I6_scaled part_06_shape_M000 part_06_shape_E3_E1 part_06_shape_E2_E1 part_06_shape_E3_E2 part_06_shape_sqrt_E1 part_06_shape_sqrt_E2 part_06_shape_sqrt_E3 part_06_density_O3 part_06_density_O4 part_06_density_O5 part_06_density_FL part_06_density_O3_norm part_06_density_O4_norm part_06_density_O5_norm part_06_density_FL_norm part_06_density_I1 part_06_density_I2 part_06_density_I3 part_06_density_I4 part_06_density_I5 part_06_density_I6 part_06_density_I1_norm part_06_density_I2_norm part_06_density_I3_norm part_06_density_I4_norm part_06_density_I5_norm part_06_density_I6_norm part_06_density_I1_scaled part_06_density_I2_scaled part_06_density_I3_scaled part_06_density_I4_scaled part_06_density_I5_scaled part_06_density_I6_scaled part_06_density_M000 part_06_density_E3_E1 part_06_density_E2_E1 part_06_density_E3_E2 part_06_density_sqrt_E1 part_06_density_sqrt_E2 part_06_density_sqrt_E3 part_07_blob_electron_sum part_07_blob_volume_sum part_07_blob_parts part_07_shape_O3 part_07_shape_O4 part_07_shape_O5 part_07_shape_FL part_07_shape_O3_norm part_07_shape_O4_norm part_07_shape_O5_norm part_07_shape_FL_norm part_07_shape_I1 part_07_shape_I2 part_07_shape_I3 part_07_shape_I4 part_07_shape_I5 part_07_shape_I6 part_07_shape_I1_norm part_07_shape_I2_norm part_07_shape_I3_norm part_07_shape_I4_norm part_07_shape_I5_norm part_07_shape_I6_norm part_07_shape_I1_scaled part_07_shape_I2_scaled part_07_shape_I3_scaled part_07_shape_I4_scaled part_07_shape_I5_scaled part_07_shape_I6_scaled part_07_shape_M000 part_07_shape_E3_E1 part_07_shape_E2_E1 part_07_shape_E3_E2 part_07_shape_sqrt_E1 part_07_shape_sqrt_E2 part_07_shape_sqrt_E3 part_07_density_O3 part_07_density_O4 part_07_density_O5 part_07_density_FL part_07_density_O3_norm part_07_density_O4_norm part_07_density_O5_norm part_07_density_FL_norm part_07_density_I1 part_07_density_I2 part_07_density_I3 part_07_density_I4 part_07_density_I5 part_07_density_I6 part_07_density_I1_norm part_07_density_I2_norm part_07_density_I3_norm part_07_density_I4_norm part_07_density_I5_norm part_07_density_I6_norm part_07_density_I1_scaled part_07_density_I2_scaled part_07_density_I3_scaled part_07_density_I4_scaled part_07_density_I5_scaled part_07_density_I6_scaled part_07_density_M000 part_07_density_E3_E1 part_07_density_E2_E1 part_07_density_E3_E2 part_07_density_sqrt_E1 part_07_density_sqrt_E2 part_07_density_sqrt_E3 part_08_blob_electron_sum part_08_blob_volume_sum part_08_blob_parts part_08_shape_O3 part_08_shape_O4 part_08_shape_O5 part_08_shape_FL part_08_shape_O3_norm part_08_shape_O4_norm part_08_shape_O5_norm part_08_shape_FL_norm part_08_shape_I1 part_08_shape_I2 part_08_shape_I3 part_08_shape_I4 part_08_shape_I5 part_08_shape_I6 part_08_shape_I1_norm part_08_shape_I2_norm part_08_shape_I3_norm part_08_shape_I4_norm part_08_shape_I5_norm part_08_shape_I6_norm part_08_shape_I1_scaled part_08_shape_I2_scaled part_08_shape_I3_scaled part_08_shape_I4_scaled part_08_shape_I5_scaled part_08_shape_I6_scaled part_08_shape_M000 part_08_shape_E3_E1 part_08_shape_E2_E1 part_08_shape_E3_E2 part_08_shape_sqrt_E1 part_08_shape_sqrt_E2 part_08_shape_sqrt_E3 part_08_density_O3 part_08_density_O4 part_08_density_O5 part_08_density_FL part_08_density_O3_norm part_08_density_O4_norm part_08_density_O5_norm part_08_density_FL_norm part_08_density_I1 part_08_density_I2 part_08_density_I3 part_08_density_I4 part_08_density_I5 part_08_density_I6 part_08_density_I1_norm part_08_density_I2_norm part_08_density_I3_norm part_08_density_I4_norm part_08_density_I5_norm part_08_density_I6_norm part_08_density_I1_scaled part_08_density_I2_scaled part_08_density_I3_scaled part_08_density_I4_scaled part_08_density_I5_scaled part_08_density_I6_scaled part_08_density_M000 part_08_density_E3_E1 part_08_density_E2_E1 part_08_density_E3_E2 part_08_density_sqrt_E1 part_08_density_sqrt_E2 part_08_density_sqrt_E3 part_09_blob_electron_sum part_09_blob_volume_sum part_09_blob_parts part_09_shape_O3 part_09_shape_O4 part_09_shape_O5 part_09_shape_FL part_09_shape_O3_norm part_09_shape_O4_norm part_09_shape_O5_norm part_09_shape_FL_norm part_09_shape_I1 part_09_shape_I2 part_09_shape_I3 part_09_shape_I4 part_09_shape_I5 part_09_shape_I6 part_09_shape_I1_norm part_09_shape_I2_norm part_09_shape_I3_norm part_09_shape_I4_norm part_09_shape_I5_norm part_09_shape_I6_norm part_09_shape_I1_scaled part_09_shape_I2_scaled part_09_shape_I3_scaled part_09_shape_I4_scaled part_09_shape_I5_scaled part_09_shape_I6_scaled part_09_shape_M000 part_09_shape_E3_E1 part_09_shape_E2_E1 part_09_shape_E3_E2 part_09_shape_sqrt_E1 part_09_shape_sqrt_E2 part_09_shape_sqrt_E3 part_09_density_O3 part_09_density_O4 part_09_density_O5 part_09_density_FL part_09_density_O3_norm part_09_density_O4_norm part_09_density_O5_norm part_09_density_FL_norm part_09_density_I1 part_09_density_I2 part_09_density_I3 part_09_density_I4 part_09_density_I5 part_09_density_I6 part_09_density_I1_norm part_09_density_I2_norm part_09_density_I3_norm part_09_density_I4_norm part_09_density_I5_norm part_09_density_I6_norm part_09_density_I1_scaled part_09_density_I2_scaled part_09_density_I3_scaled part_09_density_I4_scaled part_09_density_I5_scaled part_09_density_I6_scaled part_09_density_M000 part_09_density_E3_E1 part_09_density_E2_E1 part_09_density_E3_E2 part_09_density_sqrt_E1 part_09_density_sqrt_E2 part_09_density_sqrt_E3 local_volume local_electrons local_mean local_std local_min local_max local_skewness local_parts fo_col fc_col weight_col grid_space solvent_radius solvent_opening_radius resolution_max_limit resolution TwoFoFc_mean TwoFoFc_std TwoFoFc_square_std TwoFoFc_min TwoFoFc_max Fo_mean Fo_std Fo_square_std Fo_min Fo_max FoFc_mean FoFc_std FoFc_square_std FoFc_min FoFc_max Fc_mean Fc_std Fc_square_std Fc_min Fc_max solvent_mask_count void_mask_count modeled_mask_count solvent_ratio TwoFoFc_bulk_mean TwoFoFc_bulk_std TwoFoFc_void_mean TwoFoFc_void_std TwoFoFc_modeled_mean TwoFoFc_modeled_std Fo_bulk_mean Fo_bulk_std Fo_void_mean Fo_void_std Fo_modeled_mean Fo_modeled_std FoFc_bulk_mean FoFc_bulk_std FoFc_void_mean FoFc_void_std FoFc_modeled_mean FoFc_modeled_std Fc_bulk_mean Fc_bulk_std Fc_void_mean Fc_void_std Fc_modeled_mean Fc_modeled_std TwoFoFc_void_fit_binormal_mean1 TwoFoFc_void_fit_binormal_std1 TwoFoFc_void_fit_binormal_mean2 TwoFoFc_void_fit_binormal_std2 TwoFoFc_void_fit_binormal_scale TwoFoFc_solvent_fit_normal_mean TwoFoFc_solvent_fit_normal_std part_step_FoFc_std_min part_step_FoFc_std_max part_step_FoFc_std_step
110l BME 901 A: 1 3ag4 : 16 SO4 : 1183 301 : 430 A :8630 Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Min. : 1.00 Min. : 1.00 Min. : 0.00 Min. : 3.0 Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. :0.0000 Min. : 1.00 Min. : 3.0 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 0.000 Min. : 0.00 Min. :0.0000 Min. : 24253 Min. :1.952e+08 Min. :5.160e+11 Min. :3.330e+06 Min. :0.2312 Min. :0.0178 Min. :0.0005 Min. :0.0000 Min. :6.875e+05 Min. :1.246e+11 Min. :9.688e+10 Min. :1.335e+06 Min. :4.728e+03 Min. :5.618e+09 Min. : 0.0637 Min. : 0.0011 Min. : 0.0008 Min. :0.0000 Min. :0.0000 Min. : 0.0049 Min. :0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0e+00 Min. :0 Min. : 1023 Min. :0.0005 Min. :0.0142 Min. :0.0042 Min. : 2.930 Min. : 1.793 Min. : 0.498 Min. : 1527 Min. :7.096e+05 Min. :1.011e+08 Min. :9.595e+05 Min. :0.0593 Min. :0.0012 Min. :0.0000 Min. : 0.0000 Min. :6.548e+04 Min. :9.611e+08 Min. :1.275e+09 Min. :4.130e+05 Min. :2.366e+04 Min. :3.899e+07 Min. : 0.0048 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 47 Min. :0.0007 Min. :0.0133 Min. :0.0052 Min. : 2.499 Min. : 1.769 Min. : 0.4972 Min. : 0.000 Min. : 0.00 Min. :0.0000 Min. : 24186 Min. :1.947e+08 Min. :5.217e+11 Min. :2.023e+06 Min. :0.2313 Min. :0.0178 Min. :0.0005 Min. :0.0000 Min. :6.807e+05 Min. :1.231e+11 Min. :9.354e+10 Min. :8.184e+05 Min. :3.360e+03 Min. :5.502e+09 Min. : 0.0637 Min. : 0.0011 Min. : 0.0008 Min. :0.0000 Min. :0.0000 Min. : 0.0049 Min. :0.0001 Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.0005 Min. :0.0127 Min. :0.0044 Min. : 2.898 Min. : 1.947 Min. : 0.4978 Min. : 1038 Min. :3.280e+05 Min. :3.182e+07 Min. :3.230e+05 Min. :0.0592 Min. :0.0012 Min. :0.0000 Min. : 0.0000 Min. :3.748e+04 Min. :3.151e+08 Min. :4.195e+08 Min. :1.414e+05 Min. :1.786e+04 Min. :1.512e+07 Min. : 0.0048 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 37.76 Min. :0.0007 Min. :0.0122 Min. :0.0054 Min. : 2.496 Min. : 1.877 Min. : 0.4971 Min. : 0.00 Min. : 0.000 Min. :0.0000 Min. : 24331 Min. :1.955e+08 Min. :5.180e+11 Min. :2.142e+06 Min. :0.2312 Min. :0.0178 Min. :0.0005 Min. : 0.0000 Min. :6.937e+05 Min. :1.254e+11 Min. :1.013e+11 Min. :8.662e+05 Min. :3.121e+03 Min. :5.751e+09 Min. : 0.0637 Min. : 0.0011 Min. : 0.0008 Min. : 0.0000 Min. : 0.0000 Min. : 0.0049 Min. :0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0 Min. : 1023 Min. :0.0006 Min. :0.0138 Min. :0.0047 Min. : 2.917 Min. : 1.914 Min. : 0.4957 Min. : 4269 Min. :5.654e+06 Min. :2.385e+09 Min. :1.443e+06 Min. :0.0588 Min. :0.0011 Min. :0.0000 Min. : 0.0000 Min. :1.324e+05 Min. :3.851e+09 Min. :4.897e+09 Min. :6.988e+05 Min. :1.294e+04 Min. :2.080e+08 Min. : 0.0047 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0001 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. : 177.5 Min. :0.0008 Min. :0.0138 Min. :0.0058 Min. : 2.487 Min. : 1.857 Min. : 0.4957 Min. : 0.0 Min. : 0.00 Min. :0.0000 Min. : 24164 Min. :1.941e+08 Min. :5.143e+11 Min. :6.027e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.790e+05 Min. :1.225e+11 Min. :9.315e+10 Min. :2.433e+06 Min. :1.301e+03 Min. :5.484e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.012 Min. :0.005 Min. : 2.884 Min. : 1.817 Min. : 0.493 Min. : 4027 Min. :5.262e+06 Min. :2.243e+09 Min. :1.389e+06 Min. : 0.058 Min. : 0.001 Min. :0.000 Min. : 0.000 Min. :1.186e+05 Min. :3.489e+09 Min. :3.239e+09 Min. :5.853e+05 Min. :1.301e+04 Min. :1.669e+08 Min. : 0.005 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. : 0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. : 168 Min. :0.001 Min. :0.011 Min. :0.006 Min. : 2.473 Min. : 1.773 Min. : 0.494 Min. : 0.000 Min. : 0.0 Min. :0.0000 Min. : 24198 Min. :1.946e+08 Min. :5.198e+11 Min. :7.913e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.817e+05 Min. :1.231e+11 Min. :9.400e+10 Min. :3.183e+06 Min. :3.774e+03 Min. :5.528e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.009 Min. :0.010 Min. : 2.869 Min. : 1.644 Min. : 0.496 Min. : 6632 Min. :1.333e+07 Min. :7.807e+09 Min. :1.426e+06 Min. : 0.058 Min. : 0.001 Min. :0.000 Min. : 0.000 Min. :2.208e+05 Min. :1.088e+10 Min. :1.331e+10 Min. :5.746e+05 Min. :3.000e+00 Min. :5.871e+08 Min. : 0.005 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 260.4 Min. :0.001 Min. :0.008 Min. :0.011 Min. : 2.577 Min. : 1.617 Min. : 0.497 Min. : 0.000 Min. : 0.000 Min. :0.00 Min. : 24236 Min. :1.956e+08 Min. :5.224e+11 Min. :4.696e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.861e+05 Min. :1.254e+11 Min. :9.477e+10 Min. :1.895e+06 Min. :5.507e+03 Min. :5.553e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.008 Min. :0.011 Min. : 2.866 Min. : 2.056 Min. : 0.496 Min. : 6155 Min. :1.161e+07 Min. :6.600e+09 Min. :8.006e+05 Min. : 0.058 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :1.955e+05 Min. :9.552e+09 Min. :8.451e+09 Min. :3.227e+05 Min. :8.210e+02 Min. :4.207e+08 Min. : 0.005 Min. : 0.000 Min. : 0.0 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 234.8 Min. :0.001 Min. :0.008 Min. :0.013 Min. : 2.538 Min. : 1.980 Min. : 0.496 Min. : 0.00 Min. : 0.00 Min. :0.0000 Min. : 24358 Min. :1.971e+08 Min. :5.177e+11 Min. :7.396e+06 Min. : 0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.850e+05 Min. :1.250e+11 Min. :9.408e+10 Min. :2.975e+06 Min. :4.763e+03 Min. :5.566e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0 Min. : 1023 Min. :0.001 Min. :0.008 Min. :0.012 Min. : 2.853 Min. : 2.037 Min. : 0.496 Min. : 6900 Min. :1.522e+07 Min. :1.075e+10 Min. :2.231e+06 Min. : 0.057 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :2.044e+05 Min. :1.033e+10 Min. :1.034e+10 Min. :8.939e+05 Min. :2.507e+03 Min. :5.106e+08 Min. : 0.004 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. : 292.1 Min. :0.001 Min. :0.007 Min. :0.014 Min. : 2.523 Min. : 1.955 Min. : 0.496 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 24313 Min. :1.960e+08 Min. :5.149e+11 Min. :5.901e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.911e+05 Min. :1.255e+11 Min. :9.856e+10 Min. :2.436e+06 Min. :2.860e+03 Min. :5.679e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.006 Min. :0.007 Min. :0.018 Min. : 2.930 Min. : 1.947 Min. : 1.077 Min. : 7722 Min. :1.917e+07 Min. :1.546e+10 Min. :2.261e+06 Min. :0.057 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :2.348e+05 Min. :1.442e+10 Min. :1.161e+10 Min. :9.168e+05 Min. :6.445e+03 Min. :6.453e+08 Min. : 0.004 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 291.9 Min. :0.005 Min. :0.007 Min. :0.018 Min. : 2.610 Min. : 1.921 Min. : 1.063 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 24284 Min. :1.955e+08 Min. :5.167e+11 Min. :4.143e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :6.883e+05 Min. :1.246e+11 Min. :9.780e+10 Min. :1.767e+06 Min. :5.470e+02 Min. :5.625e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0 Min. : 1023 Min. :0.005 Min. :0.010 Min. :0.020 Min. : 2.942 Min. : 2.154 Min. : 1.051 Min. : 6023 Min. :1.180e+07 Min. :7.555e+09 Min. :1.376e+06 Min. :0.057 Min. :0.001 Min. :0.000 Min. : 0.000 Min. :1.750e+05 Min. :7.599e+09 Min. :7.022e+09 Min. :5.570e+05 Min. :8.525e+03 Min. :3.736e+08 Min. : 0.004 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0 Min. : 256.5 Min. :0.004 Min. :0.010 Min. :0.020 Min. : 2.620 Min. : 2.106 Min. : 1.041 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 24584 Min. :1.970e+08 Min. :5.129e+11 Min. :4.052e+06 Min. :0.231 Min. :0.018 Min. :0.000 Min. : 0.000 Min. :7.117e+05 Min. :1.303e+11 Min. :1.047e+11 Min. :1.669e+06 Min. :3.899e+03 Min. :5.953e+09 Min. : 0.064 Min. : 0.001 Min. : 0.001 Min. : 0.000 Min. : 0.000 Min. : 0.005 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0 Min. : 1023 Min. :0.004 Min. :0.011 Min. :0.025 Min. : 2.949 Min. : 2.144 Min. : 1.060 Min. : 7987 Min. :2.102e+07 Min. :1.388e+10 Min. :1.586e+06 Min. : 0.056 Min. : 0.001 Min. :0.000 Min. : 0.000 Min. :2.621e+05 Min. :1.642e+10 Min. :1.529e+10 Min. :6.442e+05 Min. :1.606e+04 Min. :7.249e+08 Min. : 0.004 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. : 297.4 Min. :0.004 Min. :0.010 Min. :0.025 Min. : 2.598 Min. : 2.108 Min. : 1.045 Min. : 64.77 Min. : 0.000 Min. :0.00000 Min. :0.0000 Min. :0 Min. : 0.000 Min. :0.0000 Min. :0.0000 DELFWT:14132 PHDELWT:14132 Mode:logical Min. :0.2 Min. :1.9 Min. :1.4 Min. :2 Min. :0.800 Min. :-2.992e-06 Min. :0.009962 Min. :0.0003424 Min. :-2.3692 Min. : 0.2245 Min. :-3.028e-06 Min. :0.01847 Min. :0.003589 Min. :-3.3935 Min. : 0.2999 Min. :-2.206e-06 Min. :0.009009 Min. :0.0001201 Min. :-26.24490 Min. : 0.07625 Min. :-7.867e-07 Min. :0.005332 Min. :0.000403 Min. :-3.2550 Min. : 0.2407 Min. : 0 Min. : 5472 Min. : 15008 Min. :0.0000 Min. :-0.30666 Min. :0.00930 Min. :-0.2850280 Min. :0.02783 Min. :6.595e-05 Min. :0.03485 Min. :-0.08705 Min. :0.01120 Min. :-0.24222 Min. :0.03827 Min. :-0.003379 Min. :0.05494 Min. :-0.08982 Min. :0.00893 Min. :-0.095296 Min. :0.01336 Min. :-0.11559 Min. :0.01705 Min. :-0.21683 Min. :0.00345 Min. :-0.239831 Min. :0.01529 Min. :-0.005495 Min. :0.02092 Min. :-0.66520 Min. :0.01075 Min. :0.000e+00 Min. :0.000e+00 Min. :-11.2668 Min. :-0.31511 Min. :0.00918 Min. :2.5 Min. :7.1 Min. :0.5
111l CL 173 A : 1 3abl : 15 ZN : 849 1 : 424 B :2905 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 NA’s:14132 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 30.0 1st Qu.: 30.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 1.00 1st Qu.: 30.0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 9.469 1st Qu.: 14.44 1st Qu.:1.0000 1st Qu.: 106958 1st Qu.:2.876e+09 1st Qu.:2.121e+13 1st Qu.:4.427e+10 1st Qu.:0.3173 1st Qu.:0.0275 1st Qu.:0.0007 1st Qu.:0.0021 1st Qu.:8.630e+06 1st Qu.:1.181e+13 1st Qu.:2.797e+13 1st Qu.:2.340e+10 1st Qu.:5.138e+09 1st Qu.:4.161e+11 1st Qu.: 0.1478 1st Qu.: 0.0037 1st Qu.: 0.0082 1st Qu.:0.0011 1st Qu.:0.0002 1st Qu.: 0.0202 1st Qu.:0.0007 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0e+00 1st Qu.:0 1st Qu.: 1900 1st Qu.:0.0715 1st Qu.:0.1974 1st Qu.:0.3046 1st Qu.: 5.594 1st Qu.: 3.546 1st Qu.: 2.654 1st Qu.: 62839 1st Qu.:1.048e+09 1st Qu.:4.744e+12 1st Qu.:1.340e+10 1st Qu.:0.3736 1st Qu.:0.0376 1st Qu.:0.0011 1st Qu.: 0.0033 1st Qu.:4.534e+06 1st Qu.:3.276e+12 1st Qu.:7.603e+12 1st Qu.:7.514e+09 1st Qu.:2.054e+09 1st Qu.:1.222e+11 1st Qu.: 0.2201 1st Qu.: 0.0084 1st Qu.: 0.0169 1st Qu.: 0.0020 1st Qu.: 0.0006 1st Qu.: 0.0343 1st Qu.:0.0014 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 1263 1st Qu.:0.0678 1st Qu.:0.1912 1st Qu.:0.2997 1st Qu.: 5.086 1st Qu.: 3.263 1st Qu.: 2.4659 1st Qu.: 8.581 1st Qu.: 12.77 1st Qu.:1.0000 1st Qu.: 99934 1st Qu.:2.480e+09 1st Qu.:1.702e+13 1st Qu.:3.533e+10 1st Qu.:0.3104 1st Qu.:0.0267 1st Qu.:0.0007 1st Qu.:0.0019 1st Qu.:7.759e+06 1st Qu.:9.470e+12 1st Qu.:2.146e+13 1st Qu.:1.857e+10 1st Qu.:3.874e+09 1st Qu.:3.399e+11 1st Qu.: 0.1405 1st Qu.: 0.0034 1st Qu.: 0.0072 1st Qu.:0.0010 1st Qu.:0.0002 1st Qu.: 0.0184 1st Qu.:0.0007 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1829 1st Qu.:0.0687 1st Qu.:0.1929 1st Qu.:0.2994 1st Qu.: 5.444 1st Qu.: 3.499 1st Qu.: 2.6289 1st Qu.: 61609 1st Qu.:1.002e+09 1st Qu.:4.474e+12 1st Qu.:1.122e+10 1st Qu.:0.3578 1st Qu.:0.0353 1st Qu.:0.0010 1st Qu.: 0.0027 1st Qu.:4.245e+06 1st Qu.:2.887e+12 1st Qu.:6.328e+12 1st Qu.:6.274e+09 1st Qu.:1.645e+09 1st Qu.:1.122e+11 1st Qu.: 0.1991 1st Qu.: 0.0072 1st Qu.: 0.014 1st Qu.: 0.0015 1st Qu.: 0.0004 1st Qu.: 0.0295 1st Qu.:0.0013 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 1279.76 1st Qu.:0.0653 1st Qu.:0.1865 1st Qu.:0.2938 1st Qu.: 4.912 1st Qu.: 3.223 1st Qu.: 2.4489 1st Qu.: 5.98 1st Qu.: 9.488 1st Qu.:1.0000 1st Qu.: 87509 1st Qu.:1.981e+09 1st Qu.:1.221e+13 1st Qu.:2.353e+10 1st Qu.:0.2974 1st Qu.:0.0255 1st Qu.:0.0006 1st Qu.: 0.0016 1st Qu.:6.300e+06 1st Qu.:6.696e+12 1st Qu.:1.390e+13 1st Qu.:1.204e+10 1st Qu.:2.229e+09 1st Qu.:2.418e+11 1st Qu.: 0.1256 1st Qu.: 0.0030 1st Qu.: 0.0053 1st Qu.: 0.0008 1st Qu.: 0.0001 1st Qu.: 0.0154 1st Qu.:0.0007 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0 1st Qu.: 1721 1st Qu.:0.0625 1st Qu.:0.1858 1st Qu.:0.2851 1st Qu.: 5.152 1st Qu.: 3.424 1st Qu.: 2.6169 1st Qu.: 60814 1st Qu.:9.935e+08 1st Qu.:4.544e+12 1st Qu.:8.991e+09 1st Qu.:0.3269 1st Qu.:0.0304 1st Qu.:0.0008 1st Qu.: 0.0017 1st Qu.:3.855e+06 1st Qu.:2.554e+12 1st Qu.:4.996e+12 1st Qu.:4.610e+09 1st Qu.:1.031e+09 1st Qu.:9.700e+10 1st Qu.: 0.1632 1st Qu.: 0.0050 1st Qu.: 0.009 1st Qu.: 0.0009 1st Qu.: 0.0002 1st Qu.: 0.0213 1st Qu.:0.0011 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 1345.6 1st Qu.:0.0602 1st Qu.:0.1798 1st Qu.:0.2815 1st Qu.: 4.652 1st Qu.: 3.168 1st Qu.: 2.4482 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.: 80446 1st Qu.:1.677e+09 1st Qu.:9.703e+12 1st Qu.:1.576e+10 1st Qu.:0.286 1st Qu.:0.024 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:5.225e+06 1st Qu.:4.915e+12 1st Qu.:9.243e+12 1st Qu.:7.840e+09 1st Qu.:1.139e+09 1st Qu.:1.751e+11 1st Qu.: 0.113 1st Qu.: 0.003 1st Qu.: 0.004 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.013 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1641 1st Qu.:0.058 1st Qu.:0.178 1st Qu.:0.276 1st Qu.: 4.844 1st Qu.: 3.364 1st Qu.: 2.599 1st Qu.: 60306 1st Qu.:9.973e+08 1st Qu.:4.631e+12 1st Qu.:6.872e+09 1st Qu.: 0.297 1st Qu.: 0.025 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:3.494e+06 1st Qu.:2.266e+12 1st Qu.:3.813e+12 1st Qu.:3.509e+09 1st Qu.:5.793e+08 1st Qu.:8.407e+10 1st Qu.: 0.131 1st Qu.: 0.003 1st Qu.: 0.01 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.016 1st Qu.:0.001 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1421 1st Qu.:0.057 1st Qu.:0.173 1st Qu.:0.273 1st Qu.: 4.382 1st Qu.: 3.117 1st Qu.: 2.435 1st Qu.: 0.000 1st Qu.: 0.0 1st Qu.:0.0000 1st Qu.: 71079 1st Qu.:1.326e+09 1st Qu.:6.878e+12 1st Qu.:1.046e+10 1st Qu.:0.279 1st Qu.:0.024 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:4.357e+06 1st Qu.:3.416e+12 1st Qu.:6.267e+12 1st Qu.:4.954e+09 1st Qu.:6.858e+08 1st Qu.:1.308e+11 1st Qu.: 0.105 1st Qu.: 0.002 1st Qu.: 0.003 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.011 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1532 1st Qu.:0.055 1st Qu.:0.174 1st Qu.:0.267 1st Qu.: 4.606 1st Qu.: 3.294 1st Qu.: 2.562 1st Qu.: 59308 1st Qu.:9.687e+08 1st Qu.:4.582e+12 1st Qu.:5.444e+09 1st Qu.: 0.275 1st Qu.: 0.022 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:3.176e+06 1st Qu.:1.856e+12 1st Qu.:2.981e+12 1st Qu.:2.664e+09 1st Qu.:3.907e+08 1st Qu.:7.490e+10 1st Qu.: 0.111 1st Qu.: 0.002 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.012 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1439.6 1st Qu.:0.054 1st Qu.:0.168 1st Qu.:0.265 1st Qu.: 4.201 1st Qu.: 3.066 1st Qu.: 2.413 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.00 1st Qu.: 63638 1st Qu.:1.119e+09 1st Qu.:5.454e+12 1st Qu.:7.214e+09 1st Qu.:0.273 1st Qu.:0.023 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:3.620e+06 1st Qu.:2.433e+12 1st Qu.:4.051e+12 1st Qu.:3.319e+09 1st Qu.:3.608e+08 1st Qu.:9.492e+10 1st Qu.: 0.099 1st Qu.: 0.002 1st Qu.: 0.003 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.010 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1461 1st Qu.:0.054 1st Qu.:0.169 1st Qu.:0.273 1st Qu.: 4.408 1st Qu.: 3.226 1st Qu.: 2.530 1st Qu.: 58067 1st Qu.:9.551e+08 1st Qu.:4.472e+12 1st Qu.:4.431e+09 1st Qu.: 0.253 1st Qu.:0.019 1st Qu.:0.000 1st Qu.: 0.001 1st Qu.:2.854e+06 1st Qu.:1.644e+12 1st Qu.:2.343e+12 1st Qu.:2.080e+09 1st Qu.:2.396e+08 1st Qu.:6.389e+10 1st Qu.: 0.094 1st Qu.: 0.002 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.009 1st Qu.:0.001 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1473.5 1st Qu.:0.053 1st Qu.:0.163 1st Qu.:0.271 1st Qu.: 4.032 1st Qu.: 3.000 1st Qu.: 2.393 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.: 57285 1st Qu.:9.262e+08 1st Qu.:4.274e+12 1st Qu.:4.645e+09 1st Qu.: 0.266 1st Qu.:0.022 1st Qu.:0.001 1st Qu.: 0.001 1st Qu.:2.981e+06 1st Qu.:1.764e+12 1st Qu.:2.516e+12 1st Qu.:2.130e+09 1st Qu.:2.076e+08 1st Qu.:6.809e+10 1st Qu.: 0.093 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.009 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1401 1st Qu.:0.056 1st Qu.:0.166 1st Qu.:0.287 1st Qu.: 4.190 1st Qu.: 3.164 1st Qu.: 2.517 1st Qu.: 56014 1st Qu.:9.081e+08 1st Qu.:4.089e+12 1st Qu.:3.213e+09 1st Qu.: 0.234 1st Qu.:0.017 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:2.507e+06 1st Qu.:1.371e+12 1st Qu.:1.719e+12 1st Qu.:1.521e+09 1st Qu.:1.409e+08 1st Qu.:5.238e+10 1st Qu.: 0.080 1st Qu.: 0.001 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.007 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.: 1485.3 1st Qu.:0.056 1st Qu.:0.162 1st Qu.:0.288 1st Qu.: 3.841 1st Qu.: 2.954 1st Qu.: 2.384 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 51008 1st Qu.:7.495e+08 1st Qu.:3.256e+12 1st Qu.:2.976e+09 1st Qu.:0.259 1st Qu.:0.021 1st Qu.:0.001 1st Qu.: 0.000 1st Qu.:2.419e+06 1st Qu.:1.272e+12 1st Qu.:1.559e+12 1st Qu.:1.351e+09 1st Qu.:1.110e+08 1st Qu.:4.633e+10 1st Qu.: 0.087 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.008 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1348 1st Qu.:0.061 1st Qu.:0.171 1st Qu.:0.321 1st Qu.: 3.990 1st Qu.: 3.089 1st Qu.: 2.522 1st Qu.: 53459 1st Qu.:8.456e+08 1st Qu.:3.990e+12 1st Qu.:2.255e+09 1st Qu.:0.220 1st Qu.:0.015 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:2.225e+06 1st Qu.:1.118e+12 1st Qu.:1.272e+12 1st Qu.:1.056e+09 1st Qu.:8.450e+07 1st Qu.:4.341e+10 1st Qu.: 0.069 1st Qu.: 0.001 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.006 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1511.6 1st Qu.:0.061 1st Qu.:0.165 1st Qu.:0.318 1st Qu.: 3.670 1st Qu.: 2.887 1st Qu.: 2.384 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 46145 1st Qu.:6.309e+08 1st Qu.:2.522e+12 1st Qu.:1.987e+09 1st Qu.:0.255 1st Qu.:0.021 1st Qu.:0.001 1st Qu.: 0.000 1st Qu.:2.023e+06 1st Qu.:9.101e+11 1st Qu.:1.081e+12 1st Qu.:8.812e+08 1st Qu.:6.662e+07 1st Qu.:3.486e+10 1st Qu.: 0.082 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.007 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0 1st Qu.: 1298 1st Qu.:0.071 1st Qu.:0.170 1st Qu.:0.371 1st Qu.: 3.840 1st Qu.: 3.024 1st Qu.: 2.518 1st Qu.: 52102 1st Qu.:8.038e+08 1st Qu.:3.678e+12 1st Qu.:1.671e+09 1st Qu.:0.211 1st Qu.:0.013 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:2.021e+06 1st Qu.:9.610e+11 1st Qu.:1.025e+12 1st Qu.:7.524e+08 1st Qu.:5.455e+07 1st Qu.:3.837e+10 1st Qu.: 0.062 1st Qu.: 0.001 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.005 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0 1st Qu.: 1531.5 1st Qu.:0.071 1st Qu.:0.162 1st Qu.:0.369 1st Qu.: 3.547 1st Qu.: 2.827 1st Qu.: 2.378 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 43744 1st Qu.:5.732e+08 1st Qu.:2.284e+12 1st Qu.:1.446e+09 1st Qu.:0.251 1st Qu.:0.020 1st Qu.:0.001 1st Qu.: 0.000 1st Qu.:1.885e+06 1st Qu.:8.044e+11 1st Qu.:8.956e+11 1st Qu.:6.424e+08 1st Qu.:4.822e+07 1st Qu.:3.039e+10 1st Qu.: 0.080 1st Qu.: 0.002 1st Qu.: 0.002 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.007 1st Qu.:0.001 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0 1st Qu.: 1283 1st Qu.:0.083 1st Qu.:0.164 1st Qu.:0.415 1st Qu.: 3.771 1st Qu.: 2.979 1st Qu.: 2.513 1st Qu.: 50807 1st Qu.:7.782e+08 1st Qu.:3.589e+12 1st Qu.:1.327e+09 1st Qu.: 0.205 1st Qu.: 0.013 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.:1.936e+06 1st Qu.:8.855e+11 1st Qu.:9.161e+11 1st Qu.:6.017e+08 1st Qu.:4.189e+07 1st Qu.:3.637e+10 1st Qu.: 0.058 1st Qu.: 0.001 1st Qu.: 0.001 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.004 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.: 1565.5 1st Qu.:0.082 1st Qu.:0.156 1st Qu.:0.416 1st Qu.: 3.502 1st Qu.: 2.793 1st Qu.: 2.379 1st Qu.: 222.94 1st Qu.: 9.469 1st Qu.:0.02630 1st Qu.:0.1127 1st Qu.:0 1st Qu.: 0.819 1st Qu.:0.1811 1st Qu.:1.0000 NA NA NA’s:14132 1st Qu.:0.2 1st Qu.:1.9 1st Qu.:1.4 1st Qu.:2 1st Qu.:1.800 1st Qu.:-7.560e-11 1st Qu.:0.209427 1st Qu.:0.1013822 1st Qu.:-1.1336 1st Qu.: 1.7394 1st Qu.:-6.600e-11 1st Qu.:0.20189 1st Qu.:0.103773 1st Qu.:-1.0312 1st Qu.: 1.6707 1st Qu.:-1.104e-10 1st Qu.:0.099798 1st Qu.:0.0220144 1st Qu.: -0.89931 1st Qu.: 1.29550 1st Qu.:-3.450e-11 1st Qu.:0.188691 1st Qu.:0.094683 1st Qu.:-0.9041 1st Qu.: 1.5904 1st Qu.: 303069 1st Qu.: 316088 1st Qu.: 522748 1st Qu.:0.2312 1st Qu.:-0.00873 1st Qu.:0.08642 1st Qu.:-0.1686900 1st Qu.:0.16439 1st Qu.:7.875e-02 1st Qu.:0.28159 1st Qu.:-0.00762 1st Qu.:0.05653 1st Qu.:-0.15855 1st Qu.:0.14448 1st Qu.: 0.073746 1st Qu.:0.28148 1st Qu.:-0.00317 1st Qu.:0.08416 1st Qu.:-0.028594 1st Qu.:0.10356 1st Qu.: 0.00600 1st Qu.:0.11254 1st Qu.:-0.00675 1st Qu.:0.03232 1st Qu.:-0.143829 1st Qu.:0.12733 1st Qu.: 0.066065 1st Qu.:0.26695 1st Qu.:-0.33681 1st Qu.:0.12728 1st Qu.:0.000e+00 1st Qu.:0.000e+00 1st Qu.: 0.3844 1st Qu.:-0.00788 1st Qu.:0.08475 1st Qu.:2.5 1st Qu.:7.1 1st Qu.:0.5
115l BME 901 A: 1 3abk : 14 GOL : 778 501 : 417 C : 795 NA NA NA NA NA NA NA NA NA NA NA Median : 7.00 Median : 7.00 Median : 7.00 Median : 55.0 Median : 52.00 Median : 3.000 Median : 0.00 Median : 3.000 Median :0.0000 Median : 7.00 Median : 56.0 Median : 3.00 Median : 0.000 Median : 3.000 Median :0.0000 Median : 18.614 Median : 27.30 Median :1.0000 Median : 392324 Median :3.374e+10 Median :7.154e+14 Median :1.747e+12 Median :0.4826 Median :0.0508 Median :0.0012 Median :0.0129 Median :6.683e+07 Median :5.855e+14 Median :2.106e+15 Median :1.004e+12 Median :3.677e+11 Median :1.259e+13 Median : 0.3602 Median : 0.0167 Median : 0.0633 Median :0.0074 Median :0.0026 Median : 0.0873 Median :0.0014 Median :0.0000 Median :0.0000 Median :0.0000 Median :0e+00 Median :0 Median : 3580 Median :0.1328 Median :0.3432 Median :0.5061 Median : 8.634 Median : 4.749 Median : 3.275 Median : 201961 Median :8.933e+09 Median :1.036e+14 Median :4.992e+11 Median :0.6608 Median :0.0950 Median :0.0030 Median : 0.0331 Median :3.292e+07 Median :1.356e+14 Median :5.501e+14 Median :3.324e+11 Median :1.688e+11 Median :3.315e+12 Median : 0.7094 Median : 0.0617 Median : 0.2383 Median : 0.0212 Median : 0.0092 Median : 0.2346 Median :0.0035 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median : 2421 Median :0.1277 Median :0.3387 Median :0.5086 Median : 8.278 Median : 4.407 Median : 3.0316 Median : 17.561 Median : 24.51 Median :1.0000 Median : 371987 Median :3.003e+10 Median :5.893e+14 Median :1.510e+12 Median :0.4898 Median :0.0516 Median :0.0012 Median :0.0133 Median :6.310e+07 Median :4.979e+14 Median :1.913e+15 Median :8.845e+11 Median :3.127e+11 Median :1.150e+13 Median : 0.3698 Median : 0.0172 Median : 0.0669 Median :0.0077 Median :0.0028 Median : 0.0905 Median :0.0014 Median :0.0000 Median :0.000 Median :0.0000 Median :0.000 Median :0 Median : 3438 Median :0.1310 Median :0.3406 Median :0.5083 Median : 8.615 Median : 4.687 Median : 3.2292 Median : 202072 Median :8.784e+09 Median :9.831e+13 Median :4.894e+11 Median :0.6567 Median :0.0935 Median :0.0029 Median : 0.0320 Median :3.248e+07 Median :1.308e+14 Median :5.311e+14 Median :3.206e+11 Median :1.575e+11 Median :3.324e+12 Median : 0.6992 Median : 0.0591 Median : 0.234 Median : 0.0207 Median : 0.0089 Median : 0.2290 Median :0.0035 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median : 2417.00 Median :0.1259 Median :0.3374 Median :0.5097 Median : 8.306 Median : 4.348 Median : 2.9975 Median : 14.92 Median : 18.472 Median :1.0000 Median : 353222 Median :2.632e+10 Median :4.586e+14 Median :1.271e+12 Median :0.5084 Median :0.0541 Median :0.0013 Median : 0.0147 Median :5.957e+07 Median :4.354e+14 Median :1.765e+15 Median :7.309e+11 Median :2.667e+11 Median :1.056e+13 Median : 0.3991 Median : 0.0192 Median : 0.0794 Median : 0.0086 Median : 0.0031 Median : 0.1038 Median :0.0016 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0 Median : 3199 Median :0.1240 Median :0.3398 Median :0.5108 Median : 8.686 Median : 4.593 Median : 3.1510 Median : 217227 Median :9.850e+09 Median :1.054e+14 Median :5.136e+11 Median :0.6472 Median :0.0889 Median :0.0027 Median : 0.0303 Median :3.677e+07 Median :1.554e+14 Median :7.150e+14 Median :3.379e+11 Median :1.608e+11 Median :4.074e+12 Median : 0.6657 Median : 0.0525 Median : 0.219 Median : 0.0196 Median : 0.0080 Median : 0.2166 Median :0.0034 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000 Median : 2471.8 Median :0.1191 Median :0.3397 Median :0.5135 Median : 8.412 Median : 4.310 Median : 2.9366 Median : 12.2 Median : 13.93 Median :1.0000 Median : 332182 Median :2.274e+10 Median :3.814e+14 Median :1.155e+12 Median :0.523 Median :0.056 Median :0.001 Median : 0.016 Median :5.614e+07 Median :3.576e+14 Median :1.544e+15 Median :6.599e+11 Median :2.269e+11 Median :9.433e+12 Median : 0.420 Median : 0.021 Median : 0.089 Median : 0.009 Median : 0.003 Median : 0.114 Median :0.002 Median :0.000 Median :0.00 Median :0.000 Median :0.000 Median :0 Median : 2992 Median :0.121 Median :0.346 Median :0.518 Median : 8.678 Median : 4.500 Median : 3.088 Median : 228546 Median :1.021e+10 Median :1.089e+14 Median :5.777e+11 Median : 0.628 Median : 0.084 Median :0.002 Median : 0.028 Median :3.763e+07 Median :1.593e+14 Median :7.299e+14 Median :3.627e+11 Median :1.751e+11 Median :4.446e+12 Median : 0.630 Median : 0.047 Median : 0.20 Median : 0.018 Median : 0.008 Median : 0.200 Median :0.003 Median :0.000 Median : 0.000 Median :0.00 Median :0.000 Median :0.000 Median : 2517 Median :0.117 Median :0.347 Median :0.521 Median : 8.473 Median : 4.238 Median : 2.885 Median : 9.215 Median : 10.6 Median :1.0000 Median : 280935 Median :1.629e+10 Median :2.224e+14 Median :7.137e+11 Median :0.525 Median :0.056 Median :0.001 Median : 0.015 Median :4.370e+07 Median :2.246e+14 Median :9.829e+14 Median :4.405e+11 Median :1.563e+11 Median :6.147e+12 Median : 0.429 Median : 0.021 Median : 0.091 Median : 0.009 Median : 0.003 Median : 0.116 Median :0.002 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0 Median : 2709 Median :0.119 Median :0.350 Median :0.527 Median : 8.460 Median : 4.360 Median : 3.012 Median : 208895 Median :8.384e+09 Median :8.358e+13 Median :4.365e+11 Median : 0.594 Median : 0.076 Median :0.002 Median : 0.024 Median :3.302e+07 Median :1.173e+14 Median :6.077e+14 Median :2.828e+11 Median :1.362e+11 Median :3.757e+12 Median : 0.569 Median : 0.040 Median : 0.16 Median : 0.015 Median : 0.006 Median : 0.172 Median :0.003 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median : 2479.6 Median :0.114 Median :0.348 Median :0.533 Median : 8.245 Median : 4.104 Median : 2.825 Median : 3.495 Median : 8.224 Median :1.00 Median : 235095 Median :1.149e+10 Median :1.206e+14 Median :4.385e+11 Median :0.516 Median :0.053 Median :0.001 Median : 0.014 Median :3.362e+07 Median :1.342e+14 Median :5.714e+14 Median :2.675e+11 Median :9.867e+10 Median :4.056e+12 Median : 0.406 Median : 0.019 Median : 0.081 Median : 0.008 Median : 0.003 Median : 0.108 Median :0.002 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0 Median : 2462 Median :0.121 Median :0.350 Median :0.548 Median : 8.105 Median : 4.177 Median : 2.942 Median : 191922 Median :7.194e+09 Median :6.487e+13 Median :3.234e+11 Median : 0.545 Median :0.066 Median :0.002 Median : 0.017 Median :2.862e+07 Median :8.374e+13 Median :4.300e+14 Median :2.165e+11 Median :1.037e+11 Median :3.022e+12 Median : 0.481 Median : 0.030 Median : 0.1 Median : 0.011 Median : 0.005 Median : 0.129 Median :0.003 Median :0.000 Median : 0.000 Median :0.000 Median :0.000 Median :0.000 Median : 2418.0 Median :0.117 Median :0.351 Median :0.556 Median : 7.926 Median : 3.941 Median : 2.760 Median : 0.00 Median : 0.00 Median :0.0000 Median : 185677 Median :7.099e+09 Median :6.726e+13 Median :2.095e+11 Median : 0.485 Median :0.048 Median :0.001 Median : 0.010 Median :2.342e+07 Median :6.457e+13 Median :2.739e+14 Median :1.217e+11 Median :4.539e+10 Median :2.319e+12 Median : 0.353 Median : 0.014 Median : 0.061 Median : 0.006 Median : 0.002 Median : 0.090 Median :0.002 Median :0.000 Median :0.000 Median :0.00 Median :0.000 Median :0 Median : 2232 Median :0.129 Median :0.370 Median :0.585 Median : 7.658 Median : 3.950 Median : 2.899 Median : 165427 Median :5.518e+09 Median :4.585e+13 Median :1.829e+11 Median : 0.475 Median :0.051 Median :0.001 Median : 0.009 Median :2.158e+07 Median :4.817e+13 Median :2.463e+14 Median :1.245e+11 Median :5.890e+10 Median :1.962e+12 Median : 0.357 Median : 0.017 Median : 0.06 Median : 0.006 Median : 0.003 Median : 0.085 Median :0.002 Median :0.000 Median :0.000 Median :0.000 Median :0.00 Median :0.000 Median : 2389.9 Median :0.126 Median :0.372 Median :0.594 Median : 7.482 Median : 3.700 Median : 2.717 Median : 0.000 Median : 0.000 Median :0.0000 Median : 150813 Median :4.832e+09 Median :3.949e+13 Median :8.898e+10 Median :0.441 Median :0.042 Median :0.001 Median : 0.006 Median :1.700e+07 Median :3.448e+13 Median :1.447e+14 Median :5.238e+10 Median :2.114e+10 Median :1.353e+12 Median : 0.298 Median : 0.010 Median : 0.043 Median : 0.003 Median : 0.001 Median : 0.067 Median :0.002 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0 Median : 2070 Median :0.145 Median :0.394 Median :0.637 Median : 7.263 Median : 3.706 Median : 2.857 Median : 144521 Median :4.340e+09 Median :3.527e+13 Median :9.948e+10 Median :0.414 Median :0.040 Median :0.001 Median : 0.004 Median :1.634e+07 Median :2.840e+13 Median :1.462e+14 Median :6.826e+10 Median :3.280e+10 Median :1.242e+12 Median : 0.255 Median : 0.009 Median : 0.029 Median : 0.003 Median : 0.001 Median : 0.052 Median :0.002 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0 Median : 2363.1 Median :0.140 Median :0.400 Median :0.654 Median : 7.036 Median : 3.471 Median : 2.681 Median : 0.000 Median : 0.000 Median :0.0000 Median : 127679 Median :3.534e+09 Median :2.584e+13 Median :4.158e+10 Median :0.407 Median :0.036 Median :0.001 Median : 0.003 Median :1.275e+07 Median :1.843e+13 Median :8.248e+13 Median :2.365e+10 Median :9.899e+09 Median :8.806e+11 Median : 0.254 Median : 0.008 Median : 0.031 Median : 0.002 Median : 0.001 Median : 0.052 Median :0.001 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0 Median : 1916 Median :0.158 Median :0.414 Median :0.684 Median : 6.899 Median : 3.489 Median : 2.817 Median : 127405 Median :3.506e+09 Median :2.632e+13 Median :5.500e+10 Median :0.371 Median :0.030 Median :0.001 Median : 0.002 Median :1.319e+07 Median :1.688e+13 Median :9.255e+13 Median :3.616e+10 Median :1.775e+10 Median :8.919e+11 Median : 0.198 Median : 0.005 Median : 0.017 Median : 0.001 Median : 0.001 Median : 0.037 Median :0.001 Median :0.000 Median :0.000 Median :0.000 Median :0.00 Median :0 Median : 2328.1 Median :0.156 Median :0.421 Median :0.700 Median : 6.616 Median : 3.275 Median : 2.650 Median : 0.000 Median : 0.000 Median :0.0000 Median : 116713 Median :2.822e+09 Median :1.990e+13 Median :2.428e+10 Median :0.392 Median :0.034 Median :0.001 Median : 0.002 Median :1.118e+07 Median :1.360e+13 Median :6.380e+13 Median :1.480e+10 Median :5.684e+09 Median :7.004e+11 Median : 0.234 Median : 0.006 Median : 0.027 Median : 0.001 Median : 0.000 Median : 0.046 Median :0.001 Median :0.000 Median :0.000 Median :0.000 Median :0.00 Median :0 Median : 1847 Median :0.167 Median :0.413 Median :0.722 Median : 6.706 Median : 3.360 Median : 2.773 Median : 124612 Median :3.209e+09 Median :2.297e+13 Median :3.406e+10 Median : 0.347 Median : 0.026 Median :0.001 Median : 0.001 Median :1.204e+07 Median :1.361e+13 Median :7.942e+13 Median :2.196e+10 Median :1.126e+10 Median :7.923e+11 Median : 0.175 Median : 0.004 Median : 0.014 Median : 0.001 Median : 0.000 Median : 0.030 Median :0.001 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median :0.000 Median : 2294.0 Median :0.166 Median :0.422 Median :0.734 Median : 6.407 Median : 3.166 Median : 2.618 Median : 414.56 Median : 18.614 Median :0.03704 Median :0.1532 Median :0 Median : 1.281 Median :0.2529 Median :1.0000 NA NA NA Median :0.2 Median :1.9 Median :1.4 Median :2 Median :2.040 Median : 1.046e-10 Median :0.273372 Median :0.1720145 Median :-0.9347 Median : 2.5344 Median : 1.430e-10 Median :0.26292 Median :0.177083 Median :-0.8379 Median : 2.4168 Median : 6.200e-12 Median :0.130447 Median :0.0413683 Median : -0.71010 Median : 1.93808 Median : 1.402e-10 Median :0.245466 Median :0.163666 Median :-0.7254 Median : 2.3201 Median : 717445 Median : 514708 Median : 885746 Median :0.3320 Median :-0.00457 Median :0.11254 Median :-0.1453470 Median :0.19674 Median :8.654e-02 Median :0.35584 Median :-0.00383 Median :0.07331 Median :-0.13823 Median :0.17009 Median : 0.082145 Median :0.35579 Median :-0.00132 Median :0.10912 Median :-0.018357 Median :0.13054 Median : 0.01149 Median :0.14498 Median :-0.00305 Median :0.04169 Median :-0.126363 Median :0.15054 Median : 0.074527 Median :0.33948 Median :-0.30237 Median :0.14707 Median :0.000e+00 Median :0.000e+00 Median : 0.4401 Median :-0.00367 Median :0.11066 Median :2.5 Median :7.1 Median :0.5
115l CL 173 A : 1 2afh : 11 CA : 661 401 : 395 D : 552 NA NA NA NA NA NA NA NA NA NA NA Mean : 13.59 Mean : 13.43 Mean : 13.05 Mean :101.5 Mean : 98.33 Mean : 7.332 Mean : 1.35 Mean : 3.817 Mean :0.2208 Mean : 13.74 Mean :103.8 Mean : 7.47 Mean : 1.357 Mean : 3.979 Mean :0.2213 Mean : 31.535 Mean : 51.45 Mean :0.9725 Mean : 3091661 Mean :1.622e+13 Mean :1.938e+20 Mean :2.123e+16 Mean :0.5752 Mean :0.0782 Mean :0.0026 Mean :0.0651 Mean :4.023e+09 Mean :1.143e+20 Mean :6.415e+20 Mean :1.253e+16 Mean :6.720e+15 Mean :1.592e+17 Mean : 0.6958 Mean : 0.1159 Mean : 1.0010 Mean :0.0416 Mean :0.0260 Mean : 0.4266 Mean :0.0022 Mean :0.0000 Mean :0.0000 Mean :0.0000 Mean :0e+00 Mean :0 Mean : 6622 Mean :0.2174 Mean :0.4061 Mean :0.5133 Mean :10.851 Mean : 5.807 Mean : 3.682 Mean : 1670086 Mean :3.188e+12 Mean :4.092e+18 Mean :3.482e+15 Mean :0.7665 Mean :0.1466 Mean :0.0071 Mean : 0.2521 Mean :2.036e+09 Mean :1.025e+19 Mean :7.811e+19 Mean :2.193e+15 Mean :1.334e+15 Mean :2.463e+16 Mean : 1.4490 Mean : 0.6412 Mean : 6.6229 Mean : 0.1849 Mean : 0.1401 Mean : 1.4628 Mean :0.0078 Mean :0.0000 Mean :0.0004 Mean :0.0001 Mean :0.0001 Mean :0.0000 Mean : 4059 Mean :0.2206 Mean :0.4058 Mean :0.5151 Mean :10.400 Mean : 5.487 Mean : 3.4481 Mean : 29.751 Mean : 46.67 Mean :0.9522 Mean : 2894498 Mean :1.384e+13 Mean :1.558e+20 Mean :1.829e+16 Mean :0.5905 Mean :0.0823 Mean :0.0028 Mean :0.0756 Mean :3.739e+09 Mean :9.661e+19 Mean :5.336e+20 Mean :1.094e+16 Mean :6.034e+15 Mean :1.347e+17 Mean : 0.7492 Mean : 0.1351 Mean : 1.2288 Mean :0.0491 Mean :0.0315 Mean : 0.4913 Mean :0.0024 Mean :0.0000 Mean :0.000 Mean :0.0000 Mean :0.000 Mean :0 Mean : 6285 Mean :0.2203 Mean :0.4073 Mean :0.5139 Mean :10.825 Mean : 5.750 Mean : 3.6288 Mean : 1627744 Mean :2.936e+12 Mean :3.562e+18 Mean :3.237e+15 Mean :0.7698 Mean :0.1473 Mean :0.0071 Mean : 0.2734 Mean :1.973e+09 Mean :9.264e+18 Mean :7.123e+19 Mean :2.063e+15 Mean :1.281e+15 Mean :2.274e+16 Mean : 1.4893 Mean : 0.6688 Mean : 7.645 Mean : 0.2013 Mean : 0.1533 Mean : 1.5884 Mean :0.0079 Mean :0.0000 Mean :0.0004 Mean :0.0001 Mean :0.0001 Mean :0.0000 Mean : 4006.64 Mean :0.2236 Mean :0.4073 Mean :0.5159 Mean :10.396 Mean : 5.447 Mean : 3.4090 Mean : 25.29 Mean : 36.283 Mean :0.8789 Mean : 2498087 Mean :9.441e+12 Mean :9.121e+19 Mean :1.266e+16 Mean :0.6297 Mean :0.0941 Mean :0.0033 Mean : 0.1094 Mean :3.159e+09 Mean :6.363e+19 Mean :3.286e+20 Mean :7.812e+15 Mean :4.583e+15 Mean :8.826e+16 Mean : 0.8958 Mean : 0.2128 Mean : 2.0358 Mean : 0.0736 Mean : 0.0498 Mean : 0.6871 Mean :0.0030 Mean :0.0000 Mean :0.0000 Mean :0.0000 Mean :0.0000 Mean :0 Mean : 5623 Mean :0.2274 Mean :0.4111 Mean :0.5145 Mean :10.830 Mean : 5.664 Mean : 3.5406 Mean : 1535388 Mean :2.373e+12 Mean :2.490e+18 Mean :2.667e+15 Mean :0.7767 Mean :0.1502 Mean :0.0071 Mean : 0.3481 Mean :1.830e+09 Mean :7.042e+18 Mean :5.556e+19 Mean :1.749e+15 Mean :1.137e+15 Mean :1.846e+16 Mean : 1.5867 Mean : 0.8918 Mean : 10.868 Mean : 0.2686 Mean : 0.2157 Mean : 1.8945 Mean :0.0081 Mean :0.0000 Mean :0.0005 Mean :0.0001 Mean :0.0001 Mean :0.0000 Mean : 3919.5 Mean :0.2311 Mean :0.4116 Mean :0.5172 Mean :10.432 Mean : 5.387 Mean : 3.3448 Mean : 21.1 Mean : 28.00 Mean :0.7844 Mean : 2143093 Mean :6.474e+12 Mean :5.200e+19 Mean :9.143e+15 Mean :0.664 Mean :0.105 Mean :0.004 Mean : 0.147 Mean :2.643e+09 Mean :4.120e+19 Mean :2.049e+20 Mean :5.802e+15 Mean :3.575e+15 Mean :5.861e+16 Mean : 1.043 Mean : 0.320 Mean : 3.088 Mean : 0.100 Mean : 0.068 Mean : 0.928 Mean :0.004 Mean :0.000 Mean :0.00 Mean :0.000 Mean :0.000 Mean :0 Mean : 5042 Mean :0.236 Mean :0.417 Mean :0.518 Mean :10.758 Mean : 5.557 Mean : 3.458 Mean : 1416666 Mean :1.856e+12 Mean :1.666e+18 Mean :2.191e+15 Mean : 0.780 Mean : 0.153 Mean :0.007 Mean : 0.434 Mean :1.652e+09 Mean :5.106e+18 Mean :4.232e+19 Mean :1.487e+15 Mean :1.019e+15 Mean :1.456e+16 Mean : 1.669 Mean : 1.234 Mean : 19.76 Mean : 0.334 Mean : 0.268 Mean : 2.430 Mean :0.009 Mean :0.000 Mean : 0.002 Mean :0.00 Mean :0.000 Mean :0.000 Mean : 3800 Mean :0.240 Mean :0.418 Mean :0.522 Mean :10.377 Mean : 5.298 Mean : 3.279 Mean : 17.299 Mean : 21.4 Mean :0.6916 Mean : 1793267 Mean :4.402e+12 Mean :2.929e+19 Mean :6.519e+15 Mean :0.689 Mean :0.113 Mean :0.004 Mean : 0.191 Mean :2.154e+09 Mean :2.754e+19 Mean :1.289e+20 Mean :4.253e+15 Mean :2.743e+15 Mean :3.912e+16 Mean : 1.176 Mean : 0.461 Mean : 5.020 Mean : 0.136 Mean : 0.099 Mean : 1.241 Mean :0.004 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 4477 Mean :0.242 Mean :0.422 Mean :0.522 Mean :10.534 Mean : 5.386 Mean : 3.353 Mean : 1256622 Mean :1.367e+12 Mean :1.039e+18 Mean :1.695e+15 Mean : 0.771 Mean : 0.148 Mean :0.007 Mean : 0.541 Mean :1.425e+09 Mean :3.628e+18 Mean :3.046e+19 Mean :1.206e+15 Mean :8.804e+14 Mean :1.084e+16 Mean : 1.688 Mean : 1.227 Mean : 25.74 Mean : 0.456 Mean : 0.400 Mean : 2.607 Mean :0.008 Mean :0.000 Mean :0.001 Mean :0.000 Mean :0.000 Mean :0.000 Mean : 3618.9 Mean :0.246 Mean :0.423 Mean :0.526 Mean :10.167 Mean : 5.141 Mean : 3.188 Mean : 13.862 Mean : 16.100 Mean :0.59 Mean : 1489327 Mean :2.966e+12 Mean :1.528e+19 Mean :4.724e+15 Mean :0.699 Mean :0.116 Mean :0.004 Mean : 0.253 Mean :1.726e+09 Mean :1.838e+19 Mean :8.501e+19 Mean :3.188e+15 Mean :2.164e+15 Mean :2.655e+16 Mean : 1.268 Mean : 0.495 Mean : 7.081 Mean : 0.193 Mean : 0.153 Mean : 1.495 Mean :0.005 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 4002 Mean :0.251 Mean :0.427 Mean :0.532 Mean :10.218 Mean : 5.179 Mean : 3.254 Mean : 1092621 Mean :9.870e+11 Mean :6.227e+17 Mean :1.339e+15 Mean : 0.753 Mean :0.141 Mean :0.006 Mean : 1.045 Mean :1.196e+09 Mean :2.495e+18 Mean :2.148e+19 Mean :9.911e+14 Mean :7.593e+14 Mean :7.930e+15 Mean : 1.737 Mean : 0.956 Mean : 73.8 Mean : 0.958 Mean : 0.900 Mean : 3.705 Mean :0.009 Mean :0.000 Mean : 0.009 Mean :0.001 Mean :0.001 Mean :0.000 Mean : 3446.0 Mean :0.256 Mean :0.428 Mean :0.537 Mean : 9.854 Mean : 4.944 Mean : 3.097 Mean : 10.88 Mean : 11.94 Mean :0.4852 Mean : 1222842 Mean :2.019e+12 Mean :8.302e+18 Mean :3.378e+15 Mean : 0.687 Mean :0.112 Mean :0.004 Mean : 0.278 Mean :1.350e+09 Mean :1.269e+19 Mean :5.687e+19 Mean :2.295e+15 Mean :1.573e+15 Mean :1.828e+16 Mean : 1.299 Mean : 1.250 Mean : 12.227 Mean : 0.224 Mean : 0.187 Mean : 1.879 Mean :0.005 Mean :0.000 Mean :0.000 Mean :0.00 Mean :0.000 Mean :0 Mean : 3606 Mean :0.266 Mean :0.437 Mean :0.550 Mean : 9.751 Mean : 4.941 Mean : 3.170 Mean : 926558 Mean :6.708e+11 Mean :3.182e+17 Mean :9.803e+14 Mean : 0.717 Mean :0.129 Mean :0.005 Mean : 0.478 Mean :9.550e+08 Mean :1.601e+18 Mean :1.355e+19 Mean :7.529e+14 Mean :6.013e+14 Mean :5.339e+15 Mean : 1.628 Mean : 3.869 Mean : 31.36 Mean : 0.415 Mean : 0.372 Mean : 2.996 Mean :0.008 Mean :0.000 Mean :0.002 Mean :0.000 Mean :0.00 Mean :0.000 Mean : 3285.4 Mean :0.271 Mean :0.439 Mean :0.555 Mean : 9.395 Mean : 4.716 Mean : 3.018 Mean : 8.499 Mean : 8.825 Mean :0.3972 Mean : 980603 Mean :1.345e+12 Mean :4.072e+18 Mean :2.389e+15 Mean :0.650 Mean :0.103 Mean :0.004 Mean : 0.315 Mean :1.037e+09 Mean :8.459e+18 Mean :3.897e+19 Mean :1.649e+15 Mean :1.156e+15 Mean :1.262e+16 Mean : 1.182 Mean : 0.447 Mean : 9.429 Mean : 0.263 Mean : 0.229 Mean : 1.570 Mean :0.005 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 3254 Mean :0.285 Mean :0.451 Mean :0.577 Mean : 9.133 Mean : 4.684 Mean : 3.096 Mean : 761473 Mean :4.467e+11 Mean :1.706e+17 Mean :6.972e+14 Mean :0.659 Mean :0.114 Mean :0.005 Mean : 0.406 Mean :7.320e+08 Mean :1.082e+18 Mean :9.105e+18 Mean :5.415e+14 Mean :4.377e+14 Mean :3.615e+15 Mean : 1.407 Mean : 0.689 Mean : 11.904 Mean : 0.343 Mean : 0.300 Mean : 2.001 Mean :0.007 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 3133.6 Mean :0.290 Mean :0.453 Mean :0.582 Mean : 8.797 Mean : 4.464 Mean : 2.948 Mean : 6.594 Mean : 6.491 Mean :0.3167 Mean : 770168 Mean :8.672e+11 Mean :1.746e+18 Mean :1.586e+15 Mean :0.599 Mean :0.088 Mean :0.003 Mean : 0.201 Mean :7.658e+08 Mean :5.362e+18 Mean :2.466e+19 Mean :1.111e+15 Mean :7.948e+14 Mean :8.283e+15 Mean : 0.996 Mean : 0.314 Mean : 5.072 Mean : 0.157 Mean : 0.128 Mean : 1.152 Mean :0.004 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0 Mean : 2951 Mean :0.302 Mean :0.462 Mean :0.604 Mean : 8.469 Mean : 4.391 Mean : 3.013 Mean : 607846 Mean :2.916e+11 Mean :8.687e+16 Mean :4.451e+14 Mean :0.596 Mean :0.097 Mean :0.004 Mean : 0.321 Mean :5.321e+08 Mean :7.080e+17 Mean :5.720e+18 Mean :3.426e+14 Mean :2.742e+14 Mean :2.388e+15 Mean : 1.205 Mean : 0.627 Mean : 8.923 Mean : 0.262 Mean : 0.223 Mean : 1.636 Mean :0.006 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.00 Mean :0 Mean : 2997.7 Mean :0.306 Mean :0.463 Mean :0.610 Mean : 8.159 Mean : 4.179 Mean : 2.866 Mean : 5.125 Mean : 4.805 Mean :0.2491 Mean : 615842 Mean :5.264e+11 Mean :5.428e+17 Mean :1.288e+15 Mean :0.564 Mean :0.077 Mean :0.003 Mean : 0.197 Mean :5.785e+08 Mean :3.064e+18 Mean :1.607e+19 Mean :8.853e+14 Mean :6.166e+14 Mean :5.382e+15 Mean : 0.908 Mean : 0.327 Mean : 6.447 Mean : 0.167 Mean : 0.147 Mean : 1.216 Mean :0.004 Mean :0.000 Mean :0.000 Mean :0.000 Mean :0.00 Mean :0 Mean : 2720 Mean :0.314 Mean :0.464 Mean :0.631 Mean : 7.959 Mean : 4.151 Mean : 2.954 Mean : 498566 Mean :1.960e+11 Mean :4.007e+16 Mean :3.507e+14 Mean : 0.550 Mean : 0.085 Mean :0.004 Mean : 0.349 Mean :4.013e+08 Mean :4.416e+17 Mean :4.001e+18 Mean :2.741e+14 Mean :2.230e+14 Mean :1.661e+15 Mean : 1.116 Mean : 1.051 Mean : 14.992 Mean : 0.297 Mean : 0.262 Mean : 1.927 Mean :0.006 Mean :0.000 Mean :0.001 Mean :0.000 Mean :0.000 Mean :0.000 Mean : 2900.9 Mean :0.318 Mean :0.465 Mean :0.637 Mean : 7.671 Mean : 3.948 Mean : 2.810 Mean : 937.24 Mean : 31.535 Mean :0.04484 Mean :0.1815 Mean :0 Mean : 1.690 Mean :0.3039 Mean :0.9725 NA NA NA Mean :0.2 Mean :1.9 Mean :1.4 Mean :2 Mean :2.128 Mean :-7.000e-11 Mean :0.275025 Mean :0.1927164 Mean :-0.9678 Mean : 2.7854 Mean : 2.942e-09 Mean :0.26458 Mean :0.197404 Mean :-0.8724 Mean : 2.5077 Mean :-5.264e-10 Mean :0.133948 Mean :0.0575110 Mean : -0.75040 Mean : 2.38074 Mean : 4.934e-10 Mean :0.247163 Mean :0.182585 Mean :-0.7575 Mean : 2.4045 Mean : 1615555 Mean : 745313 Mean : 1369266 Mean :0.3389 Mean :-0.00593 Mean :0.11494 Mean :-0.1417209 Mean :0.19472 Mean :8.550e-02 Mean :0.35322 Mean :-0.00488 Mean :0.07565 Mean :-0.13486 Mean :0.16813 Mean : 0.081655 Mean :0.35238 Mean :-0.00190 Mean :0.11144 Mean :-0.018502 Mean :0.13226 Mean : 0.01071 Mean :0.14801 Mean :-0.00403 Mean :0.04446 Mean :-0.123219 Mean :0.14792 Mean : 0.074781 Mean :0.33536 Mean :-0.28451 Mean :0.15577 Mean :1.348e+73 Mean :6.825e+71 Mean : 0.4472 Mean :-0.00524 Mean :0.11325 Mean :2.5 Mean :7.1 Mean :0.5
123l BME 901 A: 1 3a0b : 11 MG : 402 201 : 306 E : 221 NA NA NA NA NA NA NA NA NA NA NA 3rd Qu.: 21.00 3rd Qu.: 20.00 3rd Qu.: 20.00 3rd Qu.:145.0 3rd Qu.:139.15 3rd Qu.:10.000 3rd Qu.: 2.00 3rd Qu.: 5.000 3rd Qu.:0.0000 3rd Qu.: 21.00 3rd Qu.:151.0 3rd Qu.:10.00 3rd Qu.: 2.000 3rd Qu.: 6.000 3rd Qu.:0.0000 3rd Qu.: 41.434 3rd Qu.: 67.06 3rd Qu.:1.0000 3rd Qu.: 2563883 3rd Qu.:1.258e+12 3rd Qu.:1.241e+17 3rd Qu.:2.287e+14 3rd Qu.:0.7365 3rd Qu.:0.0992 3rd Qu.:0.0028 3rd Qu.:0.0521 3rd Qu.:1.197e+09 3rd Qu.:1.535e+17 3rd Qu.:7.688e+17 3rd Qu.:1.300e+14 3rd Qu.:5.116e+13 3rd Qu.:1.647e+15 3rd Qu.: 0.8471 3rd Qu.: 0.0720 3rd Qu.: 0.4041 3rd Qu.:0.0306 3rd Qu.:0.0150 3rd Qu.: 0.3408 3rd Qu.:0.0026 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0e+00 3rd Qu.:0 3rd Qu.: 8604 3rd Qu.:0.2864 3rd Qu.:0.5983 3rd Qu.:0.7264 3rd Qu.:14.739 3rd Qu.: 7.444 3rd Qu.: 4.313 3rd Qu.: 1405891 3rd Qu.:3.835e+11 3rd Qu.:2.108e+16 3rd Qu.:7.100e+13 3rd Qu.:1.0132 3rd Qu.:0.1898 3rd Qu.:0.0074 3rd Qu.: 0.1622 3rd Qu.:6.281e+08 3rd Qu.:4.085e+16 3rd Qu.:2.124e+17 3rd Qu.:4.662e+13 3rd Qu.:2.294e+13 3rd Qu.:4.635e+14 3rd Qu.: 1.6894 3rd Qu.: 0.2874 3rd Qu.: 1.6383 3rd Qu.: 0.1056 3rd Qu.: 0.0611 3rd Qu.: 0.9441 3rd Qu.:0.0077 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5322 3rd Qu.:0.3017 3rd Qu.:0.6051 3rd Qu.:0.7363 3rd Qu.:14.368 3rd Qu.: 7.143 3rd Qu.: 4.0015 3rd Qu.: 39.384 3rd Qu.: 61.21 3rd Qu.:1.0000 3rd Qu.: 2444647 3rd Qu.:1.136e+12 3rd Qu.:1.036e+17 3rd Qu.:2.211e+14 3rd Qu.:0.7633 3rd Qu.:0.1046 3rd Qu.:0.0029 3rd Qu.:0.0576 3rd Qu.:1.145e+09 3rd Qu.:1.387e+17 3rd Qu.:7.039e+17 3rd Qu.:1.258e+14 3rd Qu.:4.840e+13 3rd Qu.:1.513e+15 3rd Qu.: 0.9138 3rd Qu.: 0.0810 3rd Qu.: 0.4712 3rd Qu.:0.0344 3rd Qu.:0.0168 3rd Qu.: 0.3852 3rd Qu.:0.0029 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 8213 3rd Qu.:0.2989 3rd Qu.:0.6085 3rd Qu.:0.7338 3rd Qu.:14.839 3rd Qu.: 7.393 3rd Qu.: 4.2305 3rd Qu.: 1411425 3rd Qu.:3.754e+11 3rd Qu.:1.981e+16 3rd Qu.:7.614e+13 3rd Qu.:1.0250 3rd Qu.:0.1896 3rd Qu.:0.0073 3rd Qu.: 0.1652 3rd Qu.:6.229e+08 3rd Qu.:4.167e+16 3rd Qu.:2.155e+17 3rd Qu.:4.987e+13 3rd Qu.:2.406e+13 3rd Qu.:4.685e+14 3rd Qu.: 1.7199 3rd Qu.: 0.2869 3rd Qu.: 1.696 3rd Qu.: 0.1089 3rd Qu.: 0.0639 3rd Qu.: 0.9822 3rd Qu.:0.0078 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5277.11 3rd Qu.:0.3139 3rd Qu.:0.6152 3rd Qu.:0.7444 3rd Qu.:14.466 3rd Qu.: 7.099 3rd Qu.: 3.9472 3rd Qu.: 34.14 3rd Qu.: 47.706 3rd Qu.:1.0000 3rd Qu.: 2198054 3rd Qu.:9.254e+11 3rd Qu.:7.874e+16 3rd Qu.:1.902e+14 3rd Qu.:0.8274 3rd Qu.:0.1201 3rd Qu.:0.0033 3rd Qu.: 0.0759 3rd Qu.:1.030e+09 3rd Qu.:1.117e+17 3rd Qu.:5.691e+17 3rd Qu.:1.103e+14 3rd Qu.:4.242e+13 3rd Qu.:1.216e+15 3rd Qu.: 1.0766 3rd Qu.: 0.1077 3rd Qu.: 0.6657 3rd Qu.: 0.0462 3rd Qu.: 0.0222 3rd Qu.: 0.5005 3rd Qu.:0.0035 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0 3rd Qu.: 7378 3rd Qu.:0.3368 3rd Qu.:0.6307 3rd Qu.:0.7479 3rd Qu.:15.036 3rd Qu.: 7.290 3rd Qu.: 4.1043 3rd Qu.: 1379860 3rd Qu.:3.597e+11 3rd Qu.:1.752e+16 3rd Qu.:8.023e+13 3rd Qu.:1.0468 3rd Qu.:0.1909 3rd Qu.:0.0069 3rd Qu.: 0.1823 3rd Qu.:6.298e+08 3rd Qu.:3.960e+16 3rd Qu.:2.164e+17 3rd Qu.:5.226e+13 3rd Qu.:2.598e+13 3rd Qu.:4.794e+14 3rd Qu.: 1.7974 3rd Qu.: 0.3002 3rd Qu.: 1.901 3rd Qu.: 0.1243 3rd Qu.: 0.0725 3rd Qu.: 1.0608 3rd Qu.:0.0079 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 5181.2 3rd Qu.:0.3548 3rd Qu.:0.6380 3rd Qu.:0.7573 3rd Qu.:14.648 3rd Qu.: 7.018 3rd Qu.: 3.8449 3rd Qu.: 28.5 3rd Qu.: 36.67 3rd Qu.:1.0000 3rd Qu.: 1985591 3rd Qu.:7.154e+11 3rd Qu.:4.886e+16 3rd Qu.:1.600e+14 3rd Qu.:0.884 3rd Qu.:0.134 3rd Qu.:0.004 3rd Qu.: 0.097 3rd Qu.:9.385e+08 3rd Qu.:8.713e+16 3rd Qu.:5.017e+17 3rd Qu.:9.193e+13 3rd Qu.:3.750e+13 3rd Qu.:1.022e+15 3rd Qu.: 1.213 3rd Qu.: 0.133 3rd Qu.: 0.881 3rd Qu.: 0.058 3rd Qu.: 0.029 3rd Qu.: 0.610 3rd Qu.:0.004 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 6578 3rd Qu.:0.378 3rd Qu.:0.659 3rd Qu.:0.762 3rd Qu.:15.087 3rd Qu.: 7.121 3rd Qu.: 3.971 3rd Qu.: 1320446 3rd Qu.:3.169e+11 3rd Qu.:1.409e+16 3rd Qu.:7.506e+13 3rd Qu.: 1.068 3rd Qu.: 0.189 3rd Qu.:0.007 3rd Qu.: 0.193 3rd Qu.:6.024e+08 3rd Qu.:3.541e+16 3rd Qu.:2.111e+17 3rd Qu.:4.923e+13 3rd Qu.:2.523e+13 3rd Qu.:4.485e+14 3rd Qu.: 1.859 3rd Qu.: 0.305 3rd Qu.: 2.03 3rd Qu.: 0.132 3rd Qu.: 0.079 3rd Qu.: 1.123 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.: 0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 5018 3rd Qu.:0.400 3rd Qu.:0.667 3rd Qu.:0.773 3rd Qu.:14.673 3rd Qu.: 6.898 3rd Qu.: 3.738 3rd Qu.: 23.645 3rd Qu.: 27.2 3rd Qu.:1.0000 3rd Qu.: 1660848 3rd Qu.:4.777e+11 3rd Qu.:2.560e+16 3rd Qu.:9.978e+13 3rd Qu.:0.913 3rd Qu.:0.139 3rd Qu.:0.004 3rd Qu.: 0.109 3rd Qu.:7.484e+08 3rd Qu.:5.254e+16 3rd Qu.:3.179e+17 3rd Qu.:6.074e+13 3rd Qu.:2.617e+13 3rd Qu.:6.772e+14 3rd Qu.: 1.309 3rd Qu.: 0.153 3rd Qu.: 1.000 3rd Qu.: 0.069 3rd Qu.: 0.035 3rd Qu.: 0.675 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 5715 3rd Qu.:0.414 3rd Qu.:0.674 3rd Qu.:0.778 3rd Qu.:14.650 3rd Qu.: 6.874 3rd Qu.: 3.811 3rd Qu.: 1199231 3rd Qu.:2.535e+11 3rd Qu.:9.606e+15 3rd Qu.:5.416e+13 3rd Qu.: 1.080 3rd Qu.: 0.185 3rd Qu.:0.006 3rd Qu.: 0.198 3rd Qu.:5.172e+08 3rd Qu.:2.475e+16 3rd Qu.:1.516e+17 3rd Qu.:3.561e+13 3rd Qu.:1.979e+13 3rd Qu.:3.399e+14 3rd Qu.: 1.916 3rd Qu.: 0.292 3rd Qu.: 2.22 3rd Qu.: 0.136 3rd Qu.: 0.085 3rd Qu.: 1.187 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 4716.6 3rd Qu.:0.431 3rd Qu.:0.681 3rd Qu.:0.785 3rd Qu.:14.332 3rd Qu.: 6.642 3rd Qu.: 3.601 3rd Qu.: 19.464 3rd Qu.: 19.824 3rd Qu.:1.00 3rd Qu.: 1371273 3rd Qu.:3.140e+11 3rd Qu.:1.314e+16 3rd Qu.:5.502e+13 3rd Qu.:0.921 3rd Qu.:0.139 3rd Qu.:0.004 3rd Qu.: 0.116 3rd Qu.:5.617e+08 3rd Qu.:2.766e+16 3rd Qu.:1.793e+17 3rd Qu.:3.384e+13 3rd Qu.:1.651e+13 3rd Qu.:4.296e+14 3rd Qu.: 1.350 3rd Qu.: 0.161 3rd Qu.: 1.109 3rd Qu.: 0.074 3rd Qu.: 0.039 3rd Qu.: 0.710 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 5064 3rd Qu.:0.455 3rd Qu.:0.694 3rd Qu.:0.791 3rd Qu.:14.138 3rd Qu.: 6.550 3rd Qu.: 3.655 3rd Qu.: 1031112 3rd Qu.:1.790e+11 3rd Qu.:5.742e+15 3rd Qu.:3.455e+13 3rd Qu.: 1.052 3rd Qu.:0.176 3rd Qu.:0.006 3rd Qu.: 0.191 3rd Qu.:3.961e+08 3rd Qu.:1.451e+16 3rd Qu.:9.160e+16 3rd Qu.:2.328e+13 3rd Qu.:1.350e+13 3rd Qu.:2.183e+14 3rd Qu.: 1.817 3rd Qu.: 0.266 3rd Qu.: 2.1 3rd Qu.: 0.136 3rd Qu.: 0.089 3rd Qu.: 1.124 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.: 0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 4449.9 3rd Qu.:0.468 3rd Qu.:0.697 3rd Qu.:0.799 3rd Qu.:13.871 3rd Qu.: 6.336 3rd Qu.: 3.466 3rd Qu.: 15.80 3rd Qu.: 14.36 3rd Qu.:1.0000 3rd Qu.: 1107271 3rd Qu.:2.027e+11 3rd Qu.:7.000e+15 3rd Qu.:3.081e+13 3rd Qu.: 0.883 3rd Qu.:0.130 3rd Qu.:0.003 3rd Qu.: 0.105 3rd Qu.:3.914e+08 3rd Qu.:1.424e+16 3rd Qu.:8.976e+16 3rd Qu.:1.922e+13 3rd Qu.:9.001e+12 3rd Qu.:2.430e+14 3rd Qu.: 1.261 3rd Qu.: 0.133 3rd Qu.: 0.979 3rd Qu.: 0.068 3rd Qu.: 0.038 3rd Qu.: 0.652 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 4507 3rd Qu.:0.495 3rd Qu.:0.715 3rd Qu.:0.808 3rd Qu.:13.334 3rd Qu.: 6.186 3rd Qu.: 3.528 3rd Qu.: 866913 3rd Qu.:1.323e+11 3rd Qu.:3.651e+15 3rd Qu.:2.025e+13 3rd Qu.: 1.002 3rd Qu.:0.162 3rd Qu.:0.005 3rd Qu.: 0.171 3rd Qu.:2.906e+08 3rd Qu.:8.087e+15 3rd Qu.:4.935e+16 3rd Qu.:1.365e+13 3rd Qu.:7.566e+12 3rd Qu.:1.366e+14 3rd Qu.: 1.708 3rd Qu.: 0.224 3rd Qu.: 1.79 3rd Qu.: 0.129 3rd Qu.: 0.076 3rd Qu.: 0.981 3rd Qu.:0.008 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.: 4279.1 3rd Qu.:0.511 3rd Qu.:0.721 3rd Qu.:0.816 3rd Qu.:13.031 3rd Qu.: 5.983 3rd Qu.: 3.340 3rd Qu.: 12.412 3rd Qu.: 10.976 3rd Qu.:1.0000 3rd Qu.: 814855 3rd Qu.:1.176e+11 3rd Qu.:3.385e+15 3rd Qu.:1.206e+13 3rd Qu.:0.801 3rd Qu.:0.114 3rd Qu.:0.003 3rd Qu.: 0.080 3rd Qu.:2.398e+08 3rd Qu.:5.767e+15 3rd Qu.:3.260e+16 3rd Qu.:7.591e+12 3rd Qu.:3.179e+12 3rd Qu.:1.042e+14 3rd Qu.: 1.050 3rd Qu.: 0.095 3rd Qu.: 0.639 3rd Qu.: 0.053 3rd Qu.: 0.026 3rd Qu.: 0.471 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 4013 3rd Qu.:0.537 3rd Qu.:0.740 3rd Qu.:0.826 3rd Qu.:11.845 3rd Qu.: 5.824 3rd Qu.: 3.387 3rd Qu.: 690176 3rd Qu.:8.212e+10 3rd Qu.:1.911e+15 3rd Qu.:9.179e+12 3rd Qu.:0.903 3rd Qu.:0.139 3rd Qu.:0.004 3rd Qu.: 0.127 3rd Qu.:1.923e+08 3rd Qu.:3.656e+15 3rd Qu.:2.131e+16 3rd Qu.:5.996e+12 3rd Qu.:3.316e+12 3rd Qu.:6.995e+13 3rd Qu.: 1.407 3rd Qu.: 0.158 3rd Qu.: 1.187 3rd Qu.: 0.091 3rd Qu.: 0.054 3rd Qu.: 0.741 3rd Qu.:0.007 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 4036.0 3rd Qu.:0.554 3rd Qu.:0.747 3rd Qu.:0.834 3rd Qu.:11.634 3rd Qu.: 5.615 3rd Qu.: 3.204 3rd Qu.: 8.015 3rd Qu.: 8.848 3rd Qu.:1.0000 3rd Qu.: 572611 3rd Qu.:6.305e+10 3rd Qu.:1.417e+15 3rd Qu.:4.757e+12 3rd Qu.:0.703 3rd Qu.:0.089 3rd Qu.:0.002 3rd Qu.: 0.052 3rd Qu.:1.303e+08 3rd Qu.:1.804e+15 3rd Qu.:8.163e+15 3rd Qu.:2.646e+12 3rd Qu.:1.042e+12 3rd Qu.:3.926e+13 3rd Qu.: 0.809 3rd Qu.: 0.056 3rd Qu.: 0.372 3rd Qu.: 0.032 3rd Qu.: 0.017 3rd Qu.: 0.319 3rd Qu.:0.004 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.: 3556 3rd Qu.:0.566 3rd Qu.:0.758 3rd Qu.:0.838 3rd Qu.:10.520 3rd Qu.: 5.438 3rd Qu.: 3.241 3rd Qu.: 522346 3rd Qu.:4.874e+10 3rd Qu.:9.783e+14 3rd Qu.:4.184e+12 3rd Qu.:0.754 3rd Qu.:0.099 3rd Qu.:0.003 3rd Qu.: 0.065 3rd Qu.:1.118e+08 3rd Qu.:1.395e+15 3rd Qu.:5.903e+15 3rd Qu.:2.501e+12 3rd Qu.:1.071e+12 3rd Qu.:2.916e+13 3rd Qu.: 0.982 3rd Qu.: 0.077 3rd Qu.: 0.550 3rd Qu.: 0.047 3rd Qu.: 0.026 3rd Qu.: 0.410 3rd Qu.:0.005 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0 3rd Qu.: 3776.1 3rd Qu.:0.584 3rd Qu.:0.761 3rd Qu.:0.846 3rd Qu.:10.263 3rd Qu.: 5.200 3rd Qu.: 3.063 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.:0.0000 3rd Qu.: 407467 3rd Qu.:3.338e+10 3rd Qu.:6.203e+14 3rd Qu.:2.083e+12 3rd Qu.:0.620 3rd Qu.:0.072 3rd Qu.:0.002 3rd Qu.: 0.033 3rd Qu.:7.534e+07 3rd Qu.:6.485e+14 3rd Qu.:2.948e+15 3rd Qu.:1.143e+12 3rd Qu.:3.898e+11 3rd Qu.:1.651e+13 3rd Qu.: 0.593 3rd Qu.: 0.036 3rd Qu.: 0.198 3rd Qu.: 0.019 3rd Qu.: 0.009 3rd Qu.: 0.198 3rd Qu.:0.003 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0 3rd Qu.: 3176 3rd Qu.:0.586 3rd Qu.:0.769 3rd Qu.:0.853 3rd Qu.: 9.419 3rd Qu.: 4.989 3rd Qu.: 3.140 3rd Qu.: 389669 3rd Qu.:3.000e+10 3rd Qu.:4.772e+14 3rd Qu.:1.779e+12 3rd Qu.: 0.608 3rd Qu.: 0.074 3rd Qu.:0.002 3rd Qu.: 0.032 3rd Qu.:6.932e+07 3rd Qu.:5.867e+14 3rd Qu.:2.249e+15 3rd Qu.:1.103e+12 3rd Qu.:4.860e+11 3rd Qu.:1.376e+13 3rd Qu.: 0.583 3rd Qu.: 0.038 3rd Qu.: 0.192 3rd Qu.: 0.020 3rd Qu.: 0.011 3rd Qu.: 0.195 3rd Qu.:0.004 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.: 3583.6 3rd Qu.:0.599 3rd Qu.:0.773 3rd Qu.:0.859 3rd Qu.: 9.171 3rd Qu.: 4.796 3rd Qu.: 2.962 3rd Qu.: 1140.48 3rd Qu.: 41.434 3rd Qu.:0.05407 3rd Qu.:0.2123 3rd Qu.:0 3rd Qu.: 2.098 3rd Qu.:0.3560 3rd Qu.:1.0000 NA NA NA 3rd Qu.:0.2 3rd Qu.:1.9 3rd Qu.:1.4 3rd Qu.:2 3rd Qu.:2.401 3rd Qu.: 4.709e-10 3rd Qu.:0.337722 3rd Qu.:0.2628045 3rd Qu.:-0.7773 3rd Qu.: 3.4233 3rd Qu.: 6.750e-10 3rd Qu.:0.32442 3rd Qu.:0.269623 3rd Qu.:-0.6866 3rd Qu.: 3.1626 3rd Qu.: 1.301e-10 3rd Qu.:0.164254 3rd Qu.:0.0715205 3rd Qu.: -0.55303 3rd Qu.: 2.95817 3rd Qu.: 4.920e-10 3rd Qu.:0.304156 3rd Qu.:0.251224 3rd Qu.:-0.5877 3rd Qu.: 3.0455 3rd Qu.: 1632553 3rd Qu.: 847645 3rd Qu.: 1577760 3rd Qu.:0.4363 3rd Qu.:-0.00172 3rd Qu.:0.14024 3rd Qu.:-0.1177243 3rd Qu.:0.22674 3rd Qu.:9.420e-02 3rd Qu.:0.42618 3rd Qu.:-0.00113 3rd Qu.:0.09062 3rd Qu.:-0.11395 3rd Qu.:0.19424 3rd Qu.: 0.091062 3rd Qu.:0.42462 3rd Qu.:-0.00012 3rd Qu.:0.13623 3rd Qu.:-0.007293 3rd Qu.:0.15861 3rd Qu.: 0.01579 3rd Qu.:0.17967 3rd Qu.:-0.00043 3rd Qu.:0.05314 3rd Qu.:-0.106279 3rd Qu.:0.17112 3rd Qu.: 0.084687 3rd Qu.:0.40525 3rd Qu.:-0.23695 3rd Qu.:0.17675 3rd Qu.:0.000e+00 3rd Qu.:0.000e+00 3rd Qu.: 0.5041 3rd Qu.:-0.00096 3rd Qu.:0.13851 3rd Qu.:2.5 3rd Qu.:7.1 3rd Qu.:0.5
123l CL 173 A : 1 3g6e : 11 (Other):10021 500 : 228 H : 153 NA NA NA NA NA NA NA NA NA NA NA Max. :178.00 Max. :104.00 Max. :104.00 Max. :800.0 Max. :800.00 Max. :81.000 Max. :15.00 Max. :55.000 Max. :9.0000 Max. :128.00 Max. :866.0 Max. :81.00 Max. :20.000 Max. :55.000 Max. :9.0000 Max. :320.010 Max. :1467.68 Max. :3.0000 Max. :531191023 Max. :6.478e+16 Max. :2.263e+24 Max. :1.007e+20 Max. :2.8594 Max. :0.8164 Max. :0.0560 Max. :7.4135 Max. :2.280e+12 Max. :9.980e+23 Max. :3.152e+24 Max. :5.832e+19 Max. :3.005e+19 Max. :5.869e+20 Max. :14.2814 Max. :44.5807 Max. :179.6831 Max. :4.1117 Max. :3.5597 Max. :32.0411 Max. :0.0778 Max. :0.0014 Max. :0.0056 Max. :0.0014 Max. :8e-04 Max. :0 Max. :183460 Max. :0.9517 Max. :0.9949 Max. :1.0000 Max. :57.618 Max. :23.513 Max. :19.245 Max. :103498612 Max. :2.493e+15 Max. :1.713e+22 Max. :4.246e+18 Max. :6.2210 Max. :3.2789 Max. :0.4408 Max. :56.1583 Max. :5.444e+11 Max. :3.703e+22 Max. :2.191e+23 Max. :2.480e+18 Max. :1.500e+18 Max. :3.396e+19 Max. :55.3711 Max. :671.2784 Max. :2712.8508 Max. :54.3712 Max. :53.1798 Max. :257.4787 Max. :1.0142 Max. :0.0892 Max. :0.9735 Max. :0.1136 Max. :0.1108 Max. :0.0036 Max. :40001 Max. :0.9577 Max. :0.9973 Max. :1.0000 Max. :57.158 Max. :23.337 Max. :18.8283 Max. :308.854 Max. :1364.02 Max. :3.0000 Max. :488904295 Max. :5.504e+16 Max. :1.774e+24 Max. :8.822e+19 Max. :3.2426 Max. :0.8686 Max. :0.0658 Max. :8.7118 Max. :2.080e+12 Max. :8.316e+23 Max. :2.328e+24 Max. :5.101e+19 Max. :2.620e+19 Max. :4.918e+20 Max. :15.7489 Max. :17.9810 Max. :216.0913 Max. :5.7801 Max. :4.1458 Max. :35.0370 Max. :0.0797 Max. :0.0012 Max. :0.005 Max. :0.0016 Max. :0.001 Max. :0 Max. :170502 Max. :0.9550 Max. :0.9933 Max. :1.0000 Max. :57.498 Max. :23.530 Max. :19.1324 Max. :98387452 Max. :2.257e+15 Max. :1.476e+22 Max. :3.925e+18 Max. :6.8569 Max. :3.2948 Max. :0.4961 Max. :71.1604 Max. :4.837e+11 Max. :3.307e+22 Max. :1.741e+23 Max. :2.292e+18 Max. :1.423e+18 Max. :2.699e+19 Max. :69.6222 Max. :249.4129 Max. :4234.377 Max. :69.3275 Max. :68.1055 Max. :353.0231 Max. :0.6962 Max. :0.1087 Max. :0.3068 Max. :0.0309 Max. :0.0301 Max. :0.0052 Max. :38606.79 Max. :0.9601 Max. :0.9942 Max. :1.0000 Max. :56.999 Max. :23.302 Max. :18.7365 Max. :285.05 Max. :1126.792 Max. :6.0000 Max. :393595202 Max. :3.603e+16 Max. :9.429e+23 Max. :6.152e+19 Max. :4.1657 Max. :1.2761 Max. :0.1188 Max. :15.0771 Max. :1.635e+12 Max. :5.176e+23 Max. :1.283e+24 Max. :3.555e+19 Max. :1.823e+19 Max. :3.091e+20 Max. :25.9777 Max. :60.5935 Max. :617.9170 Max. :16.9693 Max. :19.4099 Max. :76.7622 Max. :0.1386 Max. :0.0017 Max. :0.0176 Max. :0.0066 Max. :0.0076 Max. :0 Max. :140849 Max. :0.9807 Max. :1.0000 Max. :0.9975 Max. :57.058 Max. :23.104 Max. :18.8476 Max. :85505656 Max. :1.717e+15 Max. :9.809e+21 Max. :3.126e+18 Max. :8.0310 Max. :5.9281 Max. :0.7223 Max. :175.3947 Max. :3.525e+11 Max. :2.426e+22 Max. :8.098e+22 Max. :1.825e+18 Max. :1.225e+18 Max. :1.436e+19 Max. :93.9830 Max. :662.4172 Max. :8293.848 Max. :174.6308 Max. :174.1215 Max. :562.7741 Max. :0.7774 Max. :0.0311 Max. :0.5675 Max. :0.0891 Max. :0.0928 Max. :0.0014 Max. :35631.8 Max. :0.9870 Max. :1.0000 Max. :0.9981 Max. :56.374 Max. :22.701 Max. :18.4920 Max. :259.2 Max. :912.68 Max. :8.0000 Max. :308821123 Max. :2.259e+16 Max. :4.715e+23 Max. :4.159e+19 Max. :4.964 Max. :2.782 Max. :0.279 Max. :16.600 Max. :1.252e+12 Max. :3.069e+23 Max. :7.404e+23 Max. :2.404e+19 Max. :1.234e+19 Max. :1.832e+20 Max. :27.950 Max. :87.376 Max. :627.766 Max. :12.672 Max. :11.434 Max. :92.899 Max. :0.117 Max. :0.002 Max. :0.01 Max. :0.004 Max. :0.003 Max. :0 Max. :114085 Max. :0.972 Max. :1.000 Max. :0.997 Max. :56.054 Max. :22.190 Max. :18.573 Max. :72213243 Max. :1.241e+15 Max. :6.053e+21 Max. :2.396e+18 Max. :13.448 Max. :19.719 Max. :2.609 Max. :611.380 Max. :2.923e+11 Max. :1.683e+22 Max. :5.210e+22 Max. :1.399e+18 Max. :1.016e+18 Max. :9.946e+18 Max. :276.318 Max. :2741.695 Max. :63118.30 Max. :466.450 Max. :369.830 Max. :2877.496 Max. :4.533 Max. :0.738 Max. :16.988 Max. :0.98 Max. :0.777 Max. :0.013 Max. :32406 Max. :0.979 Max. :1.000 Max. :0.999 Max. :55.183 Max. :22.311 Max. :18.240 Max. :242.292 Max. :734.9 Max. :6.0000 Max. :243030424 Max. :1.407e+16 Max. :2.311e+23 Max. :2.710e+19 Max. :6.215 Max. :2.838 Max. :0.408 Max. :50.977 Max. :9.772e+11 Max. :1.880e+23 Max. :4.504e+23 Max. :1.585e+19 Max. :8.347e+18 Max. :1.119e+20 Max. :51.132 Max. :191.662 Max. :2122.910 Max. :50.723 Max. :50.553 Max. :182.251 Max. :0.199 Max. :0.004 Max. :0.031 Max. :0.008 Max. :0.008 Max. :0 Max. :91862 Max. :0.967 Max. :1.000 Max. :1.000 Max. :50.403 Max. :22.264 Max. :18.180 Max. :60618204 Max. :8.775e+14 Max. :3.583e+21 Max. :1.761e+18 Max. :14.157 Max. :10.311 Max. :2.323 Max. :1309.539 Max. :2.437e+11 Max. :1.178e+22 Max. :3.483e+22 Max. :1.037e+18 Max. :7.782e+17 Max. :6.932e+18 Max. :327.414 Max. :2492.875 Max. :103155.64 Max. :1303.359 Max. :1299.239 Max. :3605.319 Max. :2.772 Max. :0.035 Max. :7.392 Max. :1.015 Max. :1.012 Max. :0.002 Max. :30286.4 Max. :0.977 Max. :1.000 Max. :1.000 Max. :49.514 Max. :22.358 Max. :17.884 Max. :240.712 Max. :573.448 Max. :6.00 Max. :184531954 Max. :8.164e+15 Max. :1.012e+23 Max. :1.692e+19 Max. :7.021 Max. :3.351 Max. :0.373 Max. :193.305 Max. :7.452e+11 Max. :1.104e+23 Max. :2.633e+23 Max. :1.011e+19 Max. :5.568e+18 Max. :6.424e+19 Max. :84.196 Max. :88.967 Max. :6898.659 Max. :192.202 Max. :191.467 Max. :462.156 Max. :0.354 Max. :0.002 Max. :0.122 Max. :0.052 Max. :0.052 Max. :0 Max. :71681 Max. :0.972 Max. :1.000 Max. :1.000 Max. :41.957 Max. :22.471 Max. :17.579 Max. :49064625 Max. :5.773e+14 Max. :1.892e+21 Max. :1.231e+18 Max. :19.611 Max. :6.290 Max. :0.719 Max. :4345.452 Max. :1.979e+11 Max. :7.841e+21 Max. :2.082e+22 Max. :7.361e+17 Max. :6.431e+17 Max. :4.524e+18 Max. :662.334 Max. :473.929 Max. :427358.8 Max. :4322.121 Max. :4306.567 Max. :10158.080 Max. :7.758 Max. :0.016 Max. :58.633 Max. :5.479 Max. :5.459 Max. :0.016 Max. :30089.0 Max. :0.982 Max. :1.000 Max. :1.000 Max. :41.755 Max. :22.507 Max. :17.352 Max. :238.48 Max. :450.09 Max. :6.0000 Max. :141422172 Max. :4.800e+15 Max. :4.536e+22 Max. :1.039e+19 Max. :12.078 Max. :2.802 Max. :0.231 Max. :93.727 Max. :5.683e+11 Max. :6.499e+22 Max. :1.532e+23 Max. :6.245e+18 Max. :3.483e+18 Max. :3.737e+19 Max. :157.219 Max. :3703.779 Max. :23597.343 Max. :116.724 Max. :132.056 Max. :1460.896 Max. :0.886 Max. :0.118 Max. :0.749 Max. :0.03 Max. :0.031 Max. :0 Max. :56261 Max. :0.973 Max. :1.000 Max. :1.000 Max. :46.014 Max. :22.362 Max. :17.225 Max. :39694362 Max. :3.776e+14 Max. :9.941e+20 Max. :8.397e+17 Max. :17.590 Max. :6.303 Max. :0.983 Max. :274.406 Max. :1.595e+11 Max. :5.145e+21 Max. :1.208e+22 Max. :5.500e+17 Max. :5.643e+17 Max. :2.939e+18 Max. :337.296 Max. :17045.143 Max. :108788.98 Max. :285.981 Max. :293.698 Max. :4567.181 Max. :2.779 Max. :1.157 Max. :7.385 Max. :0.214 Max. :0.22 Max. :0.003 Max. :29809.5 Max. :0.973 Max. :1.000 Max. :1.000 Max. :45.925 Max. :22.399 Max. :17.016 Max. :236.349 Max. :348.256 Max. :7.0000 Max. :103908616 Max. :2.597e+15 Max. :1.792e+22 Max. :5.927e+18 Max. :7.413 Max. :3.909 Max. :0.214 Max. :394.221 Max. :4.166e+11 Max. :3.557e+22 Max. :8.263e+22 Max. :3.604e+18 Max. :2.055e+18 Max. :1.999e+19 Max. :117.872 Max. :109.662 Max. :13570.728 Max. :405.549 Max. :413.101 Max. :611.524 Max. :0.445 Max. :0.002 Max. :0.193 Max. :0.094 Max. :0.096 Max. :0 Max. :43532 Max. :0.972 Max. :1.000 Max. :1.000 Max. :42.733 Max. :22.266 Max. :16.773 Max. :30747979 Max. :2.265e+14 Max. :4.576e+20 Max. :5.324e+17 Max. :8.417 Max. :8.882 Max. :1.425 Max. :246.942 Max. :1.233e+11 Max. :3.127e+21 Max. :7.267e+21 Max. :3.467e+17 Max. :3.558e+17 Max. :1.752e+18 Max. :83.135 Max. :282.797 Max. :6814.175 Max. :257.105 Max. :263.880 Max. :417.867 Max. :0.656 Max. :0.023 Max. :0.387 Max. :0.042 Max. :0.044 Max. :0 Max. :29543.7 Max. :0.967 Max. :1.000 Max. :1.000 Max. :41.685 Max. :22.301 Max. :16.576 Max. :234.397 Max. :316.040 Max. :6.0000 Max. :73090459 Max. :1.261e+15 Max. :5.981e+21 Max. :3.113e+18 Max. :4.457 Max. :2.474 Max. :0.286 Max. :29.382 Max. :2.967e+11 Max. :1.823e+22 Max. :4.243e+22 Max. :1.930e+18 Max. :1.141e+18 Max. :1.002e+19 Max. :26.949 Max. :49.871 Max. :638.793 Max. :17.530 Max. :14.218 Max. :92.492 Max. :0.123 Max. :0.001 Max. :0.014 Max. :0.005 Max. :0.004 Max. :0 Max. :39505 Max. :0.970 Max. :1.000 Max. :1.000 Max. :42.058 Max. :21.703 Max. :16.344 Max. :23214720 Max. :1.215e+14 Max. :1.769e+20 Max. :3.087e+17 Max. :7.025 Max. :6.600 Max. :2.241 Max. :42.231 Max. :9.229e+10 Max. :1.767e+21 Max. :4.120e+21 Max. :2.388e+17 Max. :2.438e+17 Max. :9.872e+17 Max. :56.246 Max. :474.557 Max. :2820.646 Max. :40.636 Max. :39.572 Max. :250.787 Max. :0.532 Max. :0.013 Max. :0.252 Max. :0.011 Max. :0.01 Max. :0 Max. :29299.6 Max. :0.971 Max. :1.000 Max. :1.000 Max. :41.788 Max. :21.761 Max. :16.156 Max. :232.159 Max. :283.688 Max. :4.0000 Max. :48013563 Max. :5.045e+14 Max. :1.345e+21 Max. :2.072e+18 Max. :6.653 Max. :2.235 Max. :0.397 Max. :63.114 Max. :1.992e+11 Max. :8.237e+21 Max. :2.055e+22 Max. :1.195e+18 Max. :7.140e+17 Max. :4.573e+18 Max. :54.351 Max. :115.524 Max. :2704.378 Max. :58.872 Max. :56.045 Max. :304.055 Max. :0.273 Max. :0.003 Max. :0.068 Max. :0.021 Max. :0.02 Max. :0 Max. :35461 Max. :0.972 Max. :0.993 Max. :1.000 Max. :42.217 Max. :21.431 Max. :14.392 Max. :20363317 Max. :5.384e+13 Max. :4.668e+19 Max. :2.211e+17 Max. :10.189 Max. :10.013 Max. :3.744 Max. :116.909 Max. :6.505e+10 Max. :8.788e+20 Max. :2.193e+21 Max. :2.102e+17 Max. :2.170e+17 Max. :6.758e+17 Max. :117.167 Max. :1321.551 Max. :12642.835 Max. :67.944 Max. :54.022 Max. :767.723 Max. :1.550 Max. :0.109 Max. :2.212 Max. :0.043 Max. :0.053 Max. :0.002 Max. :29019.9 Max. :0.973 Max. :0.999 Max. :1.000 Max. :41.755 Max. :21.493 Max. :14.349 Max. :20509.63 Max. :320.010 Max. :0.51469 Max. :1.7125 Max. :0 Max. :14.225 Max. :2.8667 Max. :3.0000 NA NA NA Max. :0.2 Max. :1.9 Max. :1.4 Max. :2 Max. :4.601 Max. : 1.245e-06 Max. :0.635689 Max. :1.2013200 Max. :-0.1770 Max. :16.1506 Max. : 1.078e-05 Max. :0.54304 Max. :5.386880 Max. :-0.1863 Max. :47.9604 Max. : 1.388e-06 Max. :0.430955 Max. :1.6430900 Max. : -0.07612 Max. :13.36110 Max. : 1.299e-06 Max. :0.516563 Max. :4.222430 Max. :-0.1056 Max. :42.3955 Max. :75081554 Max. :14297165 Max. :27645380 Max. :0.9915 Max. : 0.06003 Max. :0.30144 Max. :-0.0006912 Max. :0.36990 Max. :3.068e-01 Max. :0.70524 Max. : 0.05589 Max. :0.30466 Max. : 0.01291 Max. :0.33391 Max. : 0.249921 Max. :0.67106 Max. : 0.12699 Max. :0.30686 Max. : 0.058075 Max. :0.33255 Max. : 0.04754 Max. :0.46476 Max. : 0.04670 Max. :0.17221 Max. : 0.007429 Max. :0.27081 Max. : 0.259225 Max. :0.61432 Max. : 0.09531 Max. :0.39516 Max. :1.886e+77 Max. :9.550e+75 Max. : 0.9943 Max. : 0.06219 Max. :0.30440 Max. :2.5 Max. :7.1 Max. :0.5
(Other) :14126 (Other):14054 NA’s : 238 (Other):11932 (Other): 876 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA’s :19 NA’s :19 NA’s :19 NA’s :19 NA’s :19 NA’s :19 NA NA NA NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA’s :407 NA NA NA NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA’s :1015 NA NA NA NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA’s :2733 NA NA NA NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA’s :4323 NA NA NA NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA’s :5688 NA NA NA NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA’s :7026 NA NA NA NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA’s :8280 NA NA NA NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA’s :9341 NA NA NA NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA’s :10246 NA NA NA NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA’s :11011 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :77 NA’s :77 NA NA NA NA NA’s :139 NA’s :139 NA’s :139 NA’s :139 NA’s :139 NA’s :77 NA’s :77 NA NA NA

Korelacje

Ze względu na bardzo dużą liczbę zmiennych, korelacje pomiędzy wszystkimi parami bylyby trudne do wizualizacji i oceny. Z tego powodu dane zostały wyselekcjonowane i podzielone na grupy, w ramach których sprawdzono korelacje. Wszystkie poniższe wykresy zostały wygenerowane na wierszach w których w analizowanych kolumnach nie było wartości NA.

W pierwszej kolejności wybrano grupę zmiennych opisujacych podstawowe wlaściwości ligandu z pominięciem kolumn part_XX, które będą badane osobno.

M = select(data3, local_volume, local_electrons, local_mean, local_std, local_max, local_skewness, local_parts,solvent_mask_count, void_mask_count, modeled_mask_count, solvent_ratio, local_res_atom_count, local_res_atom_non_h_electron_sum, local_res_atom_non_h_count)
corrplot(cor(M), method = "circle", type = "upper")

Kolejny krok analizy dotyczyć będzie kolumn rozpoczynających się od frazy “part_”. Z powodu liczby tych kolumn należy wprowadzić pewne podziały. Naturalnym wydaje się podział danch według progów odcięcia. Pierwszy wykres przedstawia wszystkie kolumny dla progu odcięcia równego 0, czyli takie zaczynające się od “part_00”. Dla czytelności usunięto tę fazę z oznaczeń kolumn.

str <- 'part_00'
M = select(data3, starts_with(str))
M <- M[complete.cases(M),]
names(M) <- substr(names(M), nchar(str)+2, 100)
corrplot(cor(M), method = "circle", type = "upper", tl.cex = 0.6)

Na wykresie widać dużą liczbę mocnych korelacji dodatnich, występują także pojedyncze korlacje ujemne.

Korelacje dla kolejnych progów odcięcia są w większości podobne, dlatego poniżej przedstawione zostały w sposób uproszczony:

strs <- c("part_01", "part_02", "part_03", "part_04", "part_05", "part_06", "part_07", "part_08", "part_09")
layout(matrix(1:9, ncol = 3))
plots <- list()
for (str in strs) {
  M = select(data3, starts_with(str))
  M <- M[complete.cases(M),]
  names(M) <- substr(names(M), nchar(str)+2, 100)
  plots$str <- corrplot(cor(M), method = "circle", type = "upper",  cl.pos = "n", tl.pos = "n", title = str,  mar=c(0,0,1,0))
}

Klasy w analizowanym zbiorze

Analizowany zbiór zawiera bardzo dużą liczbę klas. Poniżej zaprezentowano wyliczenie tej liczby oraz pierwsze 20 klas posortowane wg liczby ligandów które do nich należą.

classes <- table(select(data3,res_name))
classes <- sort(classes, decreasing = TRUE)
length(classes)
## [1] 2696
h <- head(classes, 20)
kable(t(as.matrix(h)))
SO4 ZN GOL CA MG CL NAG PO4 HEM EDO MN ACT FAD ADP FE K ATP CU PEG NAP
1183 849 778 661 402 400 396 301 296 274 182 167 157 138 120 119 106 104 90 86

Rozkład danych wg liczby atomów i elektronów

Poniższy wykres prezentuje rozkład danych wg liczby atomów i elektronów. Oś X przedstawia elektrony, a Y - atomy.

theme_empty <- theme(axis.line=element_blank(),
      axis.text.x=element_blank(),
      axis.text.y=element_blank(),
      axis.ticks=element_blank(),
      axis.title.x=element_blank(),
      axis.title.y=element_blank(),
      legend.position="none",
      panel.background=element_blank(),
      panel.border=element_blank(),
      panel.grid.major=element_blank(),
      panel.grid.minor=element_blank(),
      plot.margin = unit(c(0,0,0,0), "lines"))

theme_scale_only <- theme(axis.line=element_blank(),
      axis.text.x=element_text(size=18),
      axis.text.y=element_text(size=18, angle=90),
      axis.title.x=element_blank(),
      axis.title.y=element_blank(),
      legend.position="none",
      panel.background=element_rect(fill = '#5e4fa2', colour = '#5e4fa2'),
      panel.grid.major=element_blank(),
      panel.grid.minor=element_blank(),
      plot.margin = unit(c(0,0,0,0), "lines"))

a <- ggplot(data3, aes(x = local_res_atom_non_h_count)) + stat_bin(binwidth = 1, fill = 'red', color= 'black')  + coord_flip() + theme_empty + xlim(c(0, 100))

e <- ggplot(data3, aes(x = local_res_atom_non_h_electron_sum)) + stat_bin(binwidth = 6, fill = 'red', color= 'black')  + theme_empty+ xlim(c(0, 650))

d <- ggplot(data3, aes(x = local_res_atom_non_h_electron_sum, y = local_res_atom_non_h_count)) + stat_density2d(aes(fill = ..level..), geom="polygon") + scale_fill_gradientn(colours=rev(rainbow(200, start=0, end=0.68))) + guides(alpha=FALSE) +  scale_x_continuous(limits=c(0, 650), breaks=seq(100, 600, 100), expand = c(0, 0)) + scale_y_continuous(limits=c(0, 100), breaks=seq(20, 100, 20), expand = c(0, 0))  + theme_scale_only

blankPlot <- ggplot()+geom_blank(aes(1,1))+
  theme(plot.background = element_blank(), 
   panel.grid.major = element_blank(),
   panel.grid.minor = element_blank(), 
   panel.border = element_blank(),
   panel.background = element_blank(),
   axis.title.x = element_blank(),
   axis.title.y = element_blank(),
   axis.text.x = element_blank(), 
   axis.text.y = element_blank(),
   axis.ticks = element_blank())

grid.arrange(e, blankPlot, d, a,  ncol=2, nrow=2, widths=c(4, 1), heights=c(1, 4))
## Warning: Removed 6 rows containing non-finite values (stat_density2d).

Niezgodność liczby atomów i elektronów

Jako miara niezgodności liczby atomów (local_res_atom_non_h_count vs dict_atom_non_h_count) i elektronów (local_res_atom_non_h_count vs dict_atom_non_h_count) dla poszczególnych klas użyta zostanie średnia.

Przygotowanie danych

M = select(data3, res_name, local_res_atom_non_h_count, dict_atom_non_h_count, local_res_atom_non_h_electron_sum, dict_atom_non_h_electron_sum)
M$electronsDiff = abs(M$local_res_atom_non_h_count - M$dict_atom_non_h_count)
M$atomsDiff = abs(M$local_res_atom_non_h_electron_sum - M$dict_atom_non_h_electron_sum)

Niezgodność liczby elektronów

ME <- aggregate(M$electronsDiff, by=list(Category=M$res_name), FUN=mean)
colnames(ME) <- c("group", "mean")
ME <- ME[order(ME$mean, decreasing = TRUE),]
kable(head(ME, 20))
group mean
2087 PEU 66.50000
2068 PC1 33.33333
1017 CPQ 33.00000
1603 JEF 33.00000
2564 VV7 33.00000
1736 M0E 32.00000
2172 PTY 31.00000
1690 LI1 29.50000
1566 IP9 27.00000
2083 PEF 27.00000
1712 LP5 26.00000
962 CDL 24.77778
1711 LP4 23.00000
1940 NKP 23.00000
2260 S0N 23.00000
995 CM5 21.00000
1777 MDS 21.00000
1941 NKQ 21.00000
2435 THF 21.00000
2394 T5C 20.00000

Niezgodność liczby atomów

ME <- aggregate(M$atomsDiff, by=list(Category=M$res_name), FUN=mean)
colnames(ME) <- c("group", "mean")
ME <- ME[order(ME$mean, decreasing = TRUE),]
kable(head(ME, 20))
group mean
2087 PEU 443.0000
1017 CPQ 225.0000
2564 VV7 224.0000
1603 JEF 213.0000
2068 PC1 211.6667
1736 M0E 196.0000
2083 PEF 188.0000
2172 PTY 186.0000
1566 IP9 185.0000
1690 LI1 183.0000
1940 NKP 161.0000
1712 LP5 160.0000
2394 T5C 153.0000
2260 S0N 150.0000
279 2J8 149.0000
1941 NKQ 149.0000
962 CDL 148.8889
995 CM5 146.0000
1777 MDS 142.0000
1711 LP4 140.0000

Rozkłady wartości kolumn part_01

M = select(data3, starts_with('part_01'))

ggplot(M, aes(x=c(M[,1]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,1]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,1])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_blob_electron_sum")

ggplot(M, aes(x=c(M[,2]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,2]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,2])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_blob_volume_sum")

ggplot(M, aes(x=c(M[,3]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,3]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,3])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_blob_parts")

M <- M[complete.cases(M),]
ggplot(M, aes(x=c(M[,4]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,4]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,4])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O3")

ggplot(M, aes(x=c(M[,5]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,5]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,5])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O4")

ggplot(M, aes(x=c(M[,6]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,6]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,6])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O5")

ggplot(M, aes(x=c(M[,7]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,7]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,7])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_FL")

ggplot(M, aes(x=c(M[,8]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,8]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,8])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O3_norm")

ggplot(M, aes(x=c(M[,9]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,9]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,9])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O4_norm")

ggplot(M, aes(x=c(M[,10]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,10]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,10])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O5_norm")

ggplot(M, aes(x=c(M[,11]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,11]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,11])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_FL_norm")

ggplot(M, aes(x=c(M[,12]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,12]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,12])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I1")

ggplot(M, aes(x=c(M[,13]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,13]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,13])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I2")

ggplot(M, aes(x=c(M[,14]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,14]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,14])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I3")

ggplot(M, aes(x=c(M[,15]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,15]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,15])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I4")

ggplot(M, aes(x=c(M[,16]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,16]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,16])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I5")

ggplot(M, aes(x=c(M[,17]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,17]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,17])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I6")

ggplot(M, aes(x=c(M[,18]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,18]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,18])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I1_norm")

ggplot(M, aes(x=c(M[,19]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,19]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,19])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I2_norm")

ggplot(M, aes(x=c(M[,20]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,20]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,20])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I3_norm")

ggplot(M, aes(x=c(M[,21]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,21]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,21])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I4_norm")

ggplot(M, aes(x=c(M[,22]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,22]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,22])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I5_norm")

ggplot(M, aes(x=c(M[,23]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,23]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,23])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I6_norm")

ggplot(M, aes(x=c(M[,24]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,24]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,24])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I1_scaled")

ggplot(M, aes(x=c(M[,25]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,25]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,25])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I2_scaled")

ggplot(M, aes(x=c(M[,26]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,26]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,26])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I3_scaled")

ggplot(M, aes(x=c(M[,27]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,27]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,27])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I4_scaled")

ggplot(M, aes(x=c(M[,28]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,28]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,28])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I5_scaled")

ggplot(M, aes(x=c(M[,29]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,29]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,29])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I6_scaled")

ggplot(M, aes(x=c(M[,30]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,30]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,30])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_M000")

ggplot(M, aes(x=c(M[,31]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,31]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,31])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_E3_E1")

ggplot(M, aes(x=c(M[,32]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,32]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,32])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_E2_E1")

ggplot(M, aes(x=c(M[,33]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,33]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,33])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_E3_E2")

ggplot(M, aes(x=c(M[,34]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,34]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,34])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_sqrt_E1")

ggplot(M, aes(x=c(M[,35]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,35]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,35])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_sqrt_E2")

ggplot(M, aes(x=c(M[,36]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,36]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,36])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_sqrt_E3")

ggplot(M, aes(x=c(M[,37]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,37]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,37])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O3")

ggplot(M, aes(x=c(M[,38]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,38]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,38])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O4")

ggplot(M, aes(x=c(M[,39]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,39]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,39])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O5")

ggplot(M, aes(x=c(M[,40]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,40]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,40])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_FL")

ggplot(M, aes(x=c(M[,41]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,41]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,41])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O3_norm")

ggplot(M, aes(x=c(M[,42]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,42]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,42])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O4_norm")

ggplot(M, aes(x=c(M[,43]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,43]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,43])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O5_norm")

ggplot(M, aes(x=c(M[,44]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,44]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,44])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_FL_norm")

ggplot(M, aes(x=c(M[,45]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,45]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,45])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I1")

ggplot(M, aes(x=c(M[,46]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,46]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,46])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I2")

ggplot(M, aes(x=c(M[,47]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,47]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,47])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I3")

ggplot(M, aes(x=c(M[,48]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,48]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,48])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I4")

ggplot(M, aes(x=c(M[,49]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,49]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,49])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I5")

ggplot(M, aes(x=c(M[,50]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,50]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,50])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I6")

ggplot(M, aes(x=c(M[,51]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,51]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,51])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I1_norm")

ggplot(M, aes(x=c(M[,52]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,52]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,52])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I2_norm")

ggplot(M, aes(x=c(M[,53]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,53]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,53])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I3_norm")

ggplot(M, aes(x=c(M[,54]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,54]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,54])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I4_norm")

ggplot(M, aes(x=c(M[,55]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,55]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,55])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I5_norm")

ggplot(M, aes(x=c(M[,56]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,56]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,56])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I6_norm")

ggplot(M, aes(x=c(M[,57]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,57]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,57])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I1_scaled")

ggplot(M, aes(x=c(M[,58]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,58]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,58])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I2_scaled")

ggplot(M, aes(x=c(M[,59]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,59]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,59])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I3_scaled")

ggplot(M, aes(x=c(M[,60]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,60]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,60])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I4_scaled")

ggplot(M, aes(x=c(M[,61]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,61]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,61])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I5_scaled")

ggplot(M, aes(x=c(M[,62]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,62]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,62])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I6_scaled")

ggplot(M, aes(x=c(M[,63]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,63]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,63])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_M000")

ggplot(M, aes(x=c(M[,64]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,64]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,64])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_E3_E1")

ggplot(M, aes(x=c(M[,65]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,65]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,65])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_E2_E1")

ggplot(M, aes(x=c(M[,66]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,66]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,66])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_E3_E2")

ggplot(M, aes(x=c(M[,67]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,67]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,67])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_sqrt_E1")

ggplot(M, aes(x=c(M[,68]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,68]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,68])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_sqrt_E2")

ggplot(M, aes(x=c(M[,69]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,69]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,69])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_sqrt_E3")

Regresja

Do przewidywania liczby atomów i elektronów zdecydowano się wykorzystać kolumny z grupy part_00. Wybrano te kolumny które mają największe korelacje z kolumną part_00_blob_electron_sum

Przewidywanie liczby elektronów

M = select(data3, one_of("local_res_atom_non_h_electron_sum", "part_00_blob_electron_sum", "part_00_shape_O3", "part_00_shape_M000", "part_00_shape_sqrt_E1", "part_00_shape_sqrt_E2", "part_00_shape_sqrt_E3", "part_00_density_O3", "part_00_density_M000"))
M <- M[complete.cases(M),]

ctrl <- trainControl(method = "repeatedcv", number = 3)
fit <- train(local_res_atom_non_h_electron_sum ~ ., data = M, method = "glm", trControl = ctrl)

predicted <- predict(fit, newdata = M)
acctual <- M$local_res_atom_non_h_electron_sum

Miary R^2 i RMSE dla predykcji liczby elektronów

cor(acctual,predicted)^2
## [1] 0.5641556
rmse(predicted,acctual)
## [1] 64.31729

Przewidywanie liczby atomów

M = select(data3, one_of("local_res_atom_non_h_count", "part_00_blob_electron_sum", "part_00_shape_O3", "part_00_shape_M000", "part_00_shape_sqrt_E1", "part_00_shape_sqrt_E2", "part_00_shape_sqrt_E3", "part_00_density_O3", "part_00_density_M000"))
M <- M[complete.cases(M),]

ctrl <- trainControl(method = "repeatedcv", number = 3)
fit <- train(local_res_atom_non_h_count ~ ., data = M, method = "glm", trControl = ctrl)

predicted <- predict(fit, newdata = M)
acctual <- M$local_res_atom_non_h_count

Miary R^2 i RMSE dla predykcji liczby atomów

cor(acctual,predicted)^2
## [1] 0.5648851
rmse(predicted,acctual)
## [1] 9.544055

Klasyfikacja

M = select(data3, matches('res_name|part_00.*|local_electrons|local_mean|local_std|local_max|local_skewness|local_parts,solvent_mask_count|void_mask_count|modeled_mask_count|solvent_ratio'))
M <- M[complete.cases(M),]

classes <- table(select(M,res_name))
classes <- sort(classes, decreasing = TRUE)
top <- head(classes, 20)

Mf <- filter(M, is.element(res_name, names(top)))
Mf$res_name <- factor(Mf$res_name)

inTraining <- createDataPartition(y = Mf$res_name, p = .5, list = FALSE)

training <- Mf[ inTraining,]
testing  <- Mf[-inTraining,]

ctrl <- trainControl(method = "repeatedcv", number = 2, repeats = 5)

fit <- train(res_name ~ part_00_blob_electron_sum+part_00_blob_volume_sum+part_00_blob_parts+part_00_shape_O3+part_00_shape_O4+part_00_shape_O5+part_00_shape_FL+part_00_shape_O3_norm+part_00_shape_O4_norm+part_00_shape_O5_norm+part_00_shape_FL_norm+part_00_shape_I1+part_00_shape_I2+part_00_shape_I3+part_00_shape_I4+part_00_shape_I5+part_00_shape_I6+part_00_shape_I1_norm+part_00_shape_I2_norm+part_00_shape_I3_norm+part_00_shape_I4_norm+part_00_shape_I5_norm+part_00_shape_I6_norm+part_00_shape_I1_scaled+part_00_shape_I2_scaled+part_00_shape_I3_scaled+part_00_shape_I4_scaled+part_00_shape_I5_scaled+part_00_shape_I6_scaled+part_00_shape_M000+part_00_shape_E3_E1+part_00_shape_E2_E1+part_00_shape_E3_E2+part_00_shape_sqrt_E1+part_00_shape_sqrt_E2+part_00_shape_sqrt_E3+part_00_density_O3+part_00_density_O4+part_00_density_O5+part_00_density_FL+part_00_density_O3_norm+part_00_density_O4_norm+part_00_density_O5_norm+part_00_density_FL_norm+part_00_density_I1+part_00_density_I2+part_00_density_I3+part_00_density_I4+part_00_density_I5+part_00_density_I6+part_00_density_I1_norm+part_00_density_I2_norm+part_00_density_I3_norm+part_00_density_I4_norm+part_00_density_I5_norm+part_00_density_I6_norm+part_00_density_I1_scaled+part_00_density_I2_scaled+part_00_density_I3_scaled+part_00_density_I4_scaled+part_00_density_I5_scaled+part_00_density_I6_scaled+part_00_density_M000+part_00_density_E3_E1+part_00_density_E2_E1+part_00_density_E3_E2+part_00_density_sqrt_E1+part_00_density_sqrt_E2+part_00_density_sqrt_E3+local_electrons+local_mean+local_std+local_max+local_skewness+void_mask_count+modeled_mask_count+solvent_ratio, data = training, method = "rf", trControl = ctrl)

predicted <- predict(fit, newdata = testing)

confusionMatrix(data = predicted, testing$res_name)
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction ACT ADP ATP  CA  CL  CU EDO FAD  FE GOL HEM   K  MG  MN NAG NAP
##        ACT  28   0   0   1   6   1   7   0   1  12   0   0   3   0   1   0
##        ADP   0  30  14   7   0   0   0   2   2   0   1   0   2   3   3   4
##        ATP   0  12  14   2   0   0   0   0   0   0   1   0   2   1   0   0
##        CA    1   0   2 172   6   3   1   0  12   9   4   7  25  19   2   0
##        CL    6   1   0   8  79   0   3   0   1   4   0   8   5   2   0   0
##        CU    0   0   0   1   0   5   0   0   1   0   0   0   0   0   0   0
##        EDO   7   0   0   4   8   0  39   0   1  33   0   1   5   1   2   0
##        FAD   0   1   2   0   0   0   0  54   0   0   2   0   0   0   4   3
##        FE    0   0   0   0   0   0   0   0   4   0   0   0   0   2   0   0
##        GOL  20   5   0  11  15   1  61   1   1 247   1   3  42   2  27   0
##        HEM   0   0   3   0   0   2   0   3   2   0 129   0   1   3   4   1
##        K     0   1   0   0   3   0   0   0   0   0   0   3   0   0   0   0
##        MG    2   3   1  18   8   3   3   1   1  16   2   4  60   3   3   0
##        MN    0   1   1   5   0   1   0   0   1   0   2   0   1   9   0   0
##        NAG   0  11   8   7   2   1   0   5   1  15   2   3  13   3 143  12
##        NAP   0   2   2   2   0   0   0   8   0   0   0   0   0   2   1  22
##        PEG   0   0   0   0   1   0   2   0   1   3   0   0   1   0   0   0
##        PO4   0   0   1   2   1   0   0   0   1   1   0   0   0   0   0   0
##        SO4  14   0   0  53  53   3  10   0   5  30   0  22  29   7   4   0
##        ZN    0   1   5  28   1  30   1   2  24   1   1   2   5  31   1   0
##           Reference
## Prediction PEG PO4 SO4  ZN
##        ACT   0   4   7   0
##        ADP   0   0   0   4
##        ATP   0   0   0   1
##        CA    0  11  18  30
##        CL    1   7   9   0
##        CU    0   0   0   4
##        EDO   4   2   4   0
##        FAD   0   0   2   0
##        FE    0   0   0   3
##        GOL  22   9  15   5
##        HEM   0   0   0   8
##        K     0   0   0   0
##        MG    0   4  10  11
##        MN    0   2   0   3
##        NAG   8   3  11   6
##        NAP   0   0   0   1
##        PEG   6   0   0   0
##        PO4   0   6  11   0
##        SO4   1  93 486  26
##        ZN    0   7   9 313
## 
## Overall Statistics
##                                          
##                Accuracy : 0.562          
##                  95% CI : (0.5449, 0.579)
##     No Information Rate : 0.1769         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.511          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: ACT Class: ADP Class: ATP Class: CA Class: CL
## Sensitivity            0.358974   0.441176   0.264151   0.53583   0.43169
## Specificity            0.986613   0.986965   0.994130   0.94948   0.98230
## Pos Pred Value         0.394366   0.416667   0.424242   0.53416   0.58955
## Neg Pred Value         0.984467   0.988191   0.988026   0.94980   0.96705
## Prevalence             0.023708   0.020669   0.016109   0.09757   0.05562
## Detection Rate         0.008511   0.009119   0.004255   0.05228   0.02401
## Detection Prevalence   0.021581   0.021884   0.010030   0.09787   0.04073
## Balanced Accuracy      0.672794   0.714071   0.629141   0.74265   0.70700
##                      Class: CU Class: EDO Class: FAD Class: FE Class: GOL
## Sensitivity           0.100000    0.30709    0.71053  0.067797    0.66577
## Specificity           0.998148    0.97724    0.99564  0.998452    0.91744
## Pos Pred Value        0.454545    0.35135    0.79412  0.444444    0.50615
## Neg Pred Value        0.986276    0.97232    0.99317  0.983237    0.95575
## Prevalence            0.015198    0.03860    0.02310  0.017933    0.11277
## Detection Rate        0.001520    0.01185    0.01641  0.001216    0.07508
## Detection Prevalence  0.003343    0.03374    0.02067  0.002736    0.14833
## Balanced Accuracy     0.549074    0.64216    0.85309  0.533125    0.79160
##                      Class: HEM  Class: K Class: MG Class: MN Class: NAG
## Sensitivity             0.88966 0.0566038   0.30928  0.102273    0.73333
## Specificity             0.99141 0.9987643   0.96996  0.994691    0.96414
## Pos Pred Value          0.82692 0.4285714   0.39216  0.346154    0.56299
## Neg Pred Value          0.99489 0.9847700   0.95728  0.975797    0.98287
## Prevalence              0.04407 0.0161094   0.05897  0.026748    0.05927
## Detection Rate          0.03921 0.0009119   0.01824  0.002736    0.04347
## Detection Prevalence    0.04742 0.0021277   0.04650  0.007903    0.07720
## Balanced Accuracy       0.94054 0.5276840   0.63962  0.548482    0.84873
##                      Class: NAP Class: PEG Class: PO4 Class: SO4 Class: ZN
## Sensitivity            0.523810   0.142857   0.040541     0.8351   0.75422
## Specificity            0.994458   0.997537   0.994589     0.8708   0.94817
## Pos Pred Value         0.550000   0.428571   0.260870     0.5813   0.67749
## Neg Pred Value         0.993846   0.989011   0.956535     0.9609   0.96393
## Prevalence             0.012766   0.012766   0.044985     0.1769   0.12614
## Detection Rate         0.006687   0.001824   0.001824     0.1477   0.09514
## Detection Prevalence   0.012158   0.004255   0.006991     0.2541   0.14043
## Balanced Accuracy      0.759134   0.570197   0.517565     0.8529   0.85120